pwc-archive/papers-with-abstracts huggingface dataset:
Planning and Operations of Mixed Fleets in Mobility-on-Demand Systems
Date: 2020-08-18 00:00:00

Automated vehicles (AVs) are expected to be beneficial for Mobility-on-Demand
(MoD), thanks to their ability of being globally coordinated. To facilitate the
steady transition towards full autonomy, we consider the transition period of
AV deployment, whereby an MoD system operates a mixed fleet of automated
vehicles (AVs) and human-driven vehicles (HVs). In such systems, AVs are
centrally coordinated by the operator, and the HVs might strategically respond
to the coordination of AVs. We devise computationally tractable strategies to
coordinate mixed fleets in MoD systems. Specifically, we model an MoD system
with a mixed fleet using a Stackelberg framework where the MoD operator serves
as the leader and human-driven vehicles serve as the followers. We develop two
models: 1) a steady-state model to analyze the properties of the problem and
determine the planning variables (e.g., compensations, prices, and the fleet
size of AVs), and 2) a time-varying model to design a real-time coordination
algorithm for AVs. The proposed models are validated using a case study
inspired by real operational data of a MoD service in Singapore. Results show
that the proposed algorithms can significantly improve system performance.
Joint Channel Assignment and Power Allocation for Multi-UAV Communication
Authors: Lingyun Zhou, Xihan Chen, Mingyi Hong, Shi Jin, Qingjiang Shi
Date: 2020-08-19 00:00:00

Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the smooth approximation and alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority.
Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming
Date: 2020-08-19 00:00:00
Tasks: Image Reconstruction, Probabilistic Programming

Single Photon Emission Computed Tomography (SPECT) is one of the nuclear
medicine imaging modalities used for functional analysis of animal or human
organs. Gamma rays emitted from the scanned body are filtered with collimators
and detected by the SPECT head. The conventional reconstruction algorithms do
not consider the uncertainty in the process introduced by the field of view of
the collimators. In this paper, we incorporate the probabilistic programming
approach for SPECT image reconstruction. No-U-Turn Sampler (NUTS) is used to
estimate the scanned object system by considering uncertainty. Results indicate
that the current work can include uncertainty in reconstruction compared to
conventional approaches like MLEM and MAP. However, reconstruction time need to
be improved for phantom sizes of 128x128x128 voxels and higher.
Development of novel algorithm to visualize blood vessels on 3D ultrasound images during liver surgery
Date: 2020-08-19 00:00:00
Tasks: Vessel Detection

Volume visualization is a method that displays three-dimensional (3D) data in
two-dimensional (2D) space. Using 3D datasets instead of 2D traditional images
improves the visualization of anatomical structures, and volume visualization
helps radiologists and surgeons to review large datasets comprehensively so
that diagnosis and treatment can be enhanced. In liver surgery, blood vessel
detection is important. Liver vessels have various shapes and due to the
presence of noise in the ultrasound images, they can be confused with noise.
Suboptimal images can sometimes lead to surgical errors where the surgeon may
cut the blood vessel in error. The ultrasound system is versatile and portable
and has the advantage of being able to be used in the operating theatre. Due to
the nature of B-mode ultrasound, 1-D transfer function volume visualization of
images cannot abrogate shadow artifacts. While multi-dimensional transfer
function improves the ability to define features of interest, the high
dimensionality in the parameter domain renders it unwieldy and difficult for
clinicians to work with. To overcome these limitations, an algorithm for volume
visualization that can provide effective 3D visualization of noisy B-mode
ultrasound images, which can be useful for clinicians, is proposed. We propose
a method that is appropriate for liver ultrasound images focusing on vessels
and tumors (if present) in order to delineate their structure and positions
clearly to preempt surgical error during operation. This method can prevent
possible errors during liver surgery by providing more detailed high quality 3D
images for clinicians. Key Words: Visualization, 3D ultrasound image, Volume
Rendering, Liver surgery, Liver vessels.
Self-Tuning State Estimation for Adaptive Truss Structures Using Strain Gauges and Camera-Based Position Measurements
Authors: Alexander Warsewa, Michael Böhm, Flavio Guerra, Julia Wagner, Tobias Haist, Cristina Tarín, Oliver Sawodny
Date: 2020-08-19 00:00:00
Tasks: Position, Sensor Fusion, State Estimation

In the context of control of smart structures, we present an approach for state estimation of adaptive buildings with active load-bearing elements. For obtaining information on structural deformation, a system composed of a digital camera and optical emitters affixed to selected nodal points is introduced as a complement to conventional strain gauge sensors. Sensor fusion for this novel combination of sensors is carried out using a Kalman filter that operates on a reduced-order structure model obtained by modal analysis. Signal delay caused by image processing is compensated for by an out-of-sequence measurement update which provides for a flexible and modular estimation algorithm. Since the camera system is very precise, a self-tuning algorithm that adjusts model along with observer parameters is introduced to reduce discrepancy between system dynamic model and actual structural behavior. We further employ optimal sensor placement to limit the number of sensors to be placed on a given structure and examine the impact on estimation accuracy. A laboratory scale model of an adaptive high-rise with actuated columns and diagonal bracings is used for experimental demonstration of the proposed estimation scheme.
A Novel Approach to Predictive Accuracy Testing in Nested Environments
Authors: Jean-Yves Pitarakis
Date: 2020-08-19 00:00:00
Tasks: valid

We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample MSE loss differentials between the two competing models, leads to nuisance parameter free Gaussian asymptotics and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g. stationary and local to unit root). A local power analysis also establishes its ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.
A 3D Motion Vector Database for Dynamic Point Clouds
Date: 2020-08-19 00:00:00
Tasks: Motion Estimation

Due to the large amount of data that point clouds represent and the
differences in geometry of successive frames, the generation of motion vectors
for an entire point cloud dataset may require a significant amount of time and
computational resources. With that in mind, we provide a 3D motion vector
database for all frames of two popular dynamic point cloud datasets. The motion
vectors were obtained through translational motion estimation procedure that
partitions the point clouds into blocks of dimensions M x M x M , and for each
block, a motion vector is estimated. Our database contains motion vectors for M
= 8 and M = 16. The goal of this work is to describe this publicly available 3D
motion vector database that can be used for different purposes, such as
compression of dynamic point clouds.
Axioms for Defeat in Democratic Elections
Authors: Wesley H. Holliday, Eric Pacuit
Date: 2020-08-15 00:00:00

We propose six axioms concerning when one candidate should defeat another in a democratic election involving two or more candidates. Five of the axioms are widely satisfied by known voting procedures. The sixth axiom is a weakening of Kenneth Arrow's famous condition of the Independence of Irrelevant Alternatives (IIA). We call this weakening Coherent IIA. We prove that the five axioms plus Coherent IIA single out a method of determining defeats studied in our recent work: Split Cycle. In particular, Split Cycle provides the most resolute definition of defeat among any satisfying the six axioms for democratic defeat. In addition, we analyze how Split Cycle escapes Arrow's Impossibility Theorem and related impossibility results.
Designing inverse dynamic controller with integral action for motion planning of surgical robot in the presence of bounded disturbances
Date: 2020-08-15 00:00:00
Tasks: Motion Planning

Robotic laparoscopic grasper is a surgical tool with minimal invasion. In
this robot, achieve goals like precise tracking, stability and disturbance
rejection are very important. In this paper, first the stages of modeling and
simulating of laparoscopic robot will be discussed and the reasons for
selecting the appropriate materials for different parts of proposed practical
robot will be explained. Inverse dynamic controller with integral action is
applied to improve the accuracy of tracking procedure for a surgical
manipulator to track a specified reference signal in the presence of tremor
that is modeled as constant bounded disturbance. Based on the disturbance
rejection scheme, tracking controller is constructed which is asymptotically
stabilizing in the sense of Lyapunov. It is shown that how under proper
assumptions; the selected schemes succeed in achieving disturbance rejection at
the input of a nonlinear system. Computer simulation results demonstrate that
accurate trajectory tracking can be achieved by using the proposed controller.
Bayesian Joint Synchronization and Localization Based on Asymmetric Time-stamp Exchange
Authors: Meysam Goodarzi, Nebojsa Maletic, Jesus Gutierrez, Eckhard Grass
Date: 2020-08-19 00:00:00
Tasks: Position

In this work, we study the joint synchronization and localization (sync&loc) of Mobile Nodes (MNs) in ultra dense networks. In particular, we deploy an asymmetric timestamp exchange mechanism between MNs and Access Nodes (ANs), that, traditionally, provides us with information about the MNs' clock offset and skew. However, information about the distance between an AN and a MN is also intrinsic to the propagation delay experienced by exchanged time-stamps. In addition, we utilize Angle of Arrival (AoA) estimation to determine the incoming direction of time-stamp exchange packets, which gives further information about the MNs' location. Finally, we employ Bayesian Recursive Filtering (BRF) to combine the aforementioned pieces of information and jointly estimate the position and clock parameters of MNs. The simulation results indicate that the Root Mean Square Errors (RMSEs) of position and clock offset estimation are kept below 1 meter and 1 ns, respectively.
Competing Persuaders in Zero-Sum Games
Authors: Dilip Ravindran, Zhihan Cui
Date: 2020-08-19 00:00:00

We study Bayesian Persuasion with multiple senders who have access to conditionally independent experiments (and possibly others). Senders have zero-sum preferences over information revealed. We characterize when any set of states can be pooled in equilibrium and when all equilibria are fully revealing. The state is fully revealed in every equilibrium if and only if sender utility functions are `globally nonlinear'. With two states, this is equivalent to some sender having nontrivial preferences. The upshot is that `most' zero-sum sender preferences result in full revelation. We explore what conditions are important for competition to result in such stark information revelation.
Recording and manipulation of vagus nerve electrical activity in chronically instrumented unanesthetized near term fetal sheep
Authors: Aude Castel, Patrick M. Burns, Javier Benito, Hai L. Liu, Shikha Kuthiala, Lucien D. Durosier, Yael S. Frank, Mingju Cao, Marilène Paquet, Gilles Fecteau, André Desrochers, Martin G. Frasch
Date: 2020-08-19 00:00:00

Background: The chronically instrumented pregnant sheep has been used as a model of human fetal development and responses to pathophysiologic stimuli. This is due to the unique amenability of the unanesthetized fetal sheep to the surgical placement and maintenance of catheters and electrodes, allowing repetitive blood sampling, substance injection, recording of bioelectrical activity, application of electric stimulation and in vivo organ imaging. Recently, there has been growing interest in pleiotropic effects of vagus nerve stimulation (VNS) on various organ systems such as innate immunity, metabolism, and appetite control. There is no approach to study this in utero and corresponding physiological understanding is scarce. New Method: Based on our previous presentation of a stable chronically instrumented unanesthetized fetal sheep model, here we describe the surgical instrumentation procedure allowing successful implantation of a cervical uni- or bilateral VNS probe with or without vagotomy. Results: In a cohort of 53 animals, we present the changes in blood gas, metabolic, and inflammatory markers during the postoperative period. We detail the design of a VNS probe which also allows recording from the nerve. We also present an example of vagus electroneurogram (VENG) recorded from the VNS probe and an analytical approach to the data. Comparison with Existing Methods: This method represents the first implementation of VENG/VNS in a large pregnant mammalian organism. Conclusions: This study describes a new surgical procedure allowing to record and manipulate chronically the vagus nerve activity in an animal model of human pregnancy.
Context-aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training
Date: 2020-08-19 00:00:00
Tasks: Sentence

Mispronunciation detection is an essential component of the Computer-Assisted
Pronunciation Training (CAPT) systems. State-of-the-art mispronunciation
detection models use Deep Neural Networks (DNN) for acoustic modeling, and a
Goodness of Pronunciation (GOP) based algorithm for pronunciation scoring.
  However, GOP based scoring models have two major limitations: i.e., (i) They
depend on forced alignment which splits the speech into phonetic segments and
independently use them for scoring, which neglects the transitions between
phonemes within the segment;
  (ii) They only focus on phonetic segments, which fails to consider the
context effects across phonemes (such as liaison, omission, incomplete plosive
sound, etc.).
  In this work, we propose the Context-aware Goodness of Pronunciation (CaGOP)
scoring model. Particularly, two factors namely the transition factor and the
duration factor are injected into CaGOP scoring.
  The transition factor identifies the transitions between phonemes and applies
them to weight the frame-wise GOP. Moreover, a self-attention based phonetic
duration modeling is proposed to introduce the duration factor into the scoring
model.
  The proposed scoring model significantly outperforms baselines, achieving 20%
and 12% relative improvement over the GOP model on the phoneme-level and
sentence-level mispronunciation detection respectively.
Self-Organized Networks with Long-Range Interactions: Tandem Darwinian Evolution of $α$ and $β$ Tubulin
Authors: J. C. Phillips
Date: 2020-08-19 00:00:00

Cytoskeletons are self-organized networks based on polymerized proteins: actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality (self-organized criticality). Our theoretical analysis uses hydropathic waves to identify and contrast the functional differences between the polymerizing $\alpha$ and $\beta$ tubulin monomers, which are similar in length and secondary structures, as well as having indistinguishable phylogenetic trees. We show how evolution has improved water-driven flexibility especially for $\alpha$ tubulin, and thus facilitated heterodimer microtubule assembly, in agreement with recent atomistic simulations and topological models. We conclude that the failure of phylogenetic analysis to identify functionally specific positive Darwinian evolution has been caused by 20th century technical limitations. These are overcome using 21st century quantitative mathematical methods based on thermodynamic scaling and hydropathic modular averaging. Our most surprising result is the identification of large level sets, especially in hydrophobic extrema, with both thermodynamically first- and second-order scaled water waves. Our calculations include explicitly long-range water-protein interactions described by fractals. We also suggest a much-needed corrective for large protein drug development costs.
Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms
Authors: Jeffrey Cohen, Alex Khan, Clark Alexander
Date: 2020-08-19 00:00:00
Tasks: Portfolio Optimization

We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from historical market data, we apply our unique problem formulation on the D-Wave Systems Inc. D-Wave 2000Q (TM) quantum annealing system (hereafter called D-Wave) to find the optimal risk vs return portfolio. We approach this first classically, then using the D-Wave, to select efficient buy and hold portfolios. Our results show that practitioners can use either classical or quantum annealing methods to select attractive portfolios. This builds upon our prior work on optimization of 40 stocks.
Total Cost of Ownership Optimization for Direct Air-to-Ground System Design
Authors: E. Dinc, M. Vondra, C. Cavdar
Date: 2020-08-20 00:00:00

Aircraft cabin is one of the last venues without mobile broadband. Considering future 5G applications and connectivity requirements, direct air-to-ground communications (DA2GC) is the only technique which can provide high capacity and low latency backhaul link for aircraft via a direct communication link. To this end, we propose an analytical framework to investigate the ground station deployment problem for DA2GC network employing multi-user beamforming with dual-polarized hybrid DA2GC antenna arrays. In addition, the proposed framework is utilized to analyze and optimize the total cost of ownership (TCO) of the DA2GC network to provide coverage for European airspace. We present the interplay between different network parameters: the number of ground stations, array size, transmit power and bandwidth, and TCO optimizing deployment parameters are calculated in order to satisfy capacity requirements. At the end, we show that, depending on the cost of different network resources, a terrestrial cellular network can be designed to cover the whole European airspace with limited number of ground stations with a certain array size, i.e., 900 and 361 antenna elements for ground station and air station, respectively.
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet
Authors: Jinglun Feng, Liang Yang, HaiYan Wang, Yifeng Song, Jizhong Xiao
Date: 2020-08-20 00:00:00
Tasks: Depth Estimation, Depth Prediction, GPR, object-detection, Object Detection

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect the subsurface objects (i.e. rebars, utility pipes) and reveal the underground scene. One of the biggest challenges in GPR based inspection is the subsurface targets reconstruction. In order to address this issue, this paper presents a 3D GPR migration and dielectric prediction system to detect and reconstruct underground targets. This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i.e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets. Our proposed DepthNet processes the GPR data by removing the noise in B-scan image as well as predicting depth of subsurface objects. For DepthNet model training and testing, we collect the real GPR data in the concrete test pit at Geophysical Survey System Inc. (GSSI) and create the synthetic GPR data by using gprMax3.0 simulator. The dataset we create includes 350 labeled GPR images. The DepthNet achieves an average accuracy of 92.64% for B-scan feature detection and an 0.112 average error for underground target depth prediction. In addition, the experimental results verify that our proposed method improve the migration accuracy and performance in generating 3D GPR image compared with the traditional migration methods.