pwc-archive/papers-with-abstracts huggingface dataset:
Development of A Fully Data-Driven Artificial Intelligence and Deep Learning for URLLC Application in 6G Wireless Systems: A Survey
Authors: Adeeb Salh, Lukman Audah, Qazwan Abdullah, Abdullah Noorsaliza, Nor Shahida Mohd Shah, Jameel Mukred, Shipun Hamzah
Date: 2021-08-04 00:00:00

The full future of the sixth generation will develop a fully data-driven that provide terabit rate per second, and adopt an average of 1000+ massive number of connections per person in 10 years 2030 virtually instantaneously. Data-driven for ultra-reliable and low latency communication is a new service paradigm provided by a new application of future sixth-generation wireless communication and network architecture, involving 100+ Gbps data rates with one millisecond latency. The key constraint is the amount of computing power available to spread massive data and well-designed artificial neural networks. Artificial Intelligence provides a new technique to design wireless networks by apply learning, predicting, and make decisions to manage the stream of big data training individuals, which provides more the capacity to transform that expert learning to develop the performance of wireless networks. We study the developing technologies that will be the driving force are artificial intelligence, communication systems to guarantee low latency. This paper aims to discuss the efficiency of the developing network and alleviate the great challenge for application scenarios and study Holographic radio, enhanced wireless channel coding, enormous Internet of Things integration, and haptic communication for virtual and augmented reality provide new services on the 6G network. Furthermore, improving a multi-level architecture for ultra-reliable and low latency in deep Learning allows for data-driven AI and 6G networks for device intelligence, as well as allowing innovations based on effective learning capabilities. These difficulties must be solved in order to meet the needs of future smart networks. Furthermore, this research categorizes various unexplored research gaps between machine learning and the sixth generation.
Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment
Authors: Zachariah J. Beasley, Les A. Piegl, Paul Rosen
Date: 2021-08-02 00:00:00
Tasks: Aspect Extraction, Form, Sentiment Analysis

Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has 1) leveraged sentiment analysis in the peer-review process to inform or validate grades or 2) utilized aspect extraction to craft a review form from what students actually communicated. Our work utilizes, rather than discards, student data from review form comments to deliver better information to the instructor. In this work, we detail the process by which we create our domain-dependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm which provides a fine-grained sentiment score from text alone. We end by analyzing validity and discussing conclusions from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses.
A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
Authors: Eslam Eldeeb, Mohammad Shehab, Hirley Alves
Date: 2021-08-02 00:00:00
Tasks: Traffic Prediction

The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine type communication (mMTC) applications. To this end, 3GPP introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart internet-of-things (IoT) applications with strict QoS constraints. We propose a novel FUG allocation based on support vector machine (SVM), First, MTC devices are prioritized using SVM classifier. Second, LSTM architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta Anomaly Benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 $\%$ when serving the target massive and critical MTC applications with a limited number of resources.
Using Large Pre-Trained Models with Cross-Modal Attention for Multi-Modal Emotion Recognition
Authors: Krishna D N
Date: 2021-08-22 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition

Recently, self-supervised pre-training has shown significant improvements in many areas of machine learning, including speech and NLP. We propose using large self-supervised pre-trained models for both audio and text modality with cross-modality attention for multimodal emotion recognition. We use Wav2Vec2.0 [1] as an audio encoder base for robust speech features extraction and the BERT model [2] as a text encoder base for better contextual representation of text. These high capacity models trained on large amounts of unlabeled data contain rich feature representations and improve the downstream task's performance. We use the cross-modal attention [3] mechanism to learn alignment between audio and text representations from self-supervised models. Cross-modal attention also helps in extracting interactive information between audio and text features. We obtain utterance-level feature representation from frame-level features using statistics pooling for both audio and text modality and combine them using the early fusion technique. Our experiments show that the proposed approach obtains a 1.88% absolute improvement in accuracy compared to the previous state-of-the-art method [3] on the IEMOCAP dataset [35]. We also conduct unimodal experiments for both audio and text modalities and compare them with previous best methods.
Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry
Authors: Markus Krönke, Christine Eilers, Desislava Dimova, Melanie Köhler, Gabriel Buschner, Lilit Mirzojan, Lemonia Konstantinidou, Marcus R. Makowski, James Nagarajah, Nassir Navab, Wolfgang Weber, Thomas Wendler
Date: 2021-08-10 00:00:00

Background: Thyroid volumetry is crucial in diagnosis, treatment and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D ultrasound and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time and accuracy. Volume reference was MRI. Methods: 28 healthy volunteers were scanned with 2D and 3D ultrasound as well as by MRI. Three physicians (MD 1, 2, 3) with different levels of experience (6, 4 and 1 a) performed three 2D ultrasound and three tracked 3D ultrasound scans on each volunteer. In the 2D scans the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) segmented the 3D thyroid lobes automatically. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced medical doctor. Results: The CNN was trained to obtain a dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 ml to 0.52 ml (MD1 vs. 2), -1.33 ml to -0.17 ml (MD1 vs. 3) and -1.89 ml to -0.70 ml (MD2 vs. 3). Paired samples t-tests showed significant differences in two comparisons for 2D and none for 3D. Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes by paired samples t-tests showed a significant difference for the 2D volumetry of all MDs, and no significant difference for 3D ultrasound. Acquisition time was significantly shorter for 3D ultrasound. Conclusion: Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times.
Non-Minimal Systems with Switching Topology: Dynamics and Controls
Authors: Farhad Aghili
Date: 2021-08-23 00:00:00

This paper presents a non-minimal order dynamics model for many analysis, simulation, and control problems of constrained mechanical systems with switching topology by making use of linear projection operator. The distinct features of this model describing dynamics of the dependent coordinates are: i) The mass matrix $\bar{M}(q)$ is always positive definite even at singular configurations; ii) matrix $\dot{\bar M} - 2 \bar{C}$ is skew symmetric, where all nonlinear terms are lumped into vector $\bar{C}(q, \dot q) \dot q$ after elimination of constraint forces. Eigenvalue analysis shows that the condition number of the constraint mass matrix can be minimized upon adequate selection of a scalar parameter called ``virtual mass'' thereby reducing the sensitivity to round-off errors in numerical computation. It follows by derivation of two oblique projection matrices for computation of constraint forces and actuation forces. It is shown that projection-based model allows feedback control of dependent coordinates which, unlike reduced-order dependent coordinates, uniquely define spatial configuration of constrained systems.
Composite Adaptive Control for Anti-Unwinding Attitude Maneuvers: An Exponential Stability Result Without Persistent Excitation
Authors: Xiaodong Shao, Qinglei Hu, Daochun Li, Yang Shi, Bowen Yi
Date: 2021-08-23 00:00:00
Tasks: parameter estimation

This paper provides an exponential stability result for the adaptive anti-unwinding attitude tracking control problem of a rigid body with uncertain but constant inertia parameters, without requiring the satisfaction of persistent excitation (PE) condition. Specifically, a composite immersion and invariance (I&I) adaptive controller is derived by integrating a prediction-error-driven learning law into the dynamically scaled I&I adaptive control framework, wherein we modify the scaling factor so that the algorithm design does not involve any dynamic gains. To avoid the unwinding problem, a barrier function is introduced as the attitude error function, along with the tactful establishment of two crucial algebra properties for exponential stability analysis. The regressor filtering method is adopted in combination with the dynamic regressor extension and mixing (DREM) procedure to acquire the prediction error using only easily obtainable signals. In particular, aiding by a constructive liner time-varying filter, the scalar regressor of DREM is extended to generate a new exciting counterpart. In this way, the derived controller is shown to permit closed-loop exponential stability without PE, in the sense that both output-tracking and parameter estimation errors exponentially converge to zero. Further, the composite learning law is augmented with a power term to achieve synchronized finite/fixed-time parameter convergence. Numerical simulations are performed to verify the theoretical findings.
QoS-Oriented Sensing-Communication-Control Co-Design for UAV-Enabled Positioning
Authors: Zijie Wang, Rongke Liu, Qirui Liu, Lincong Han
Date: 2021-08-22 00:00:00
Tasks: Scheduling

Unmanned aerial vehicle (UAV)-enabled positioning that uses UAVs as aerial anchor nodes is a promising solution for providing positioning services in harsh environments. In previous research, the state sensing and control of UAVs were either ignored or simply set to be performed continuously, resulting in system instability or waste of wireless resources. Therefore, in this article, we propose a quality-of-service (QoS)-oriented UAV-enabled positioning system based on the concept of sensing-communication-control (SCC) co-design. We first establish the mathematical models of UAV state sensing and control. Then, the influence of sensing scheduling and transmission failure on UAV stability, as well as the performance of positioning services in the presence of UAV control error, are analyzed. Based on these models and analysis results, we further study the problem of minimizing the amount of data transmitted by optimizing the sensing scheduling and blocklength allocation under the condition of satisfying each user's demand. Finally, an efficient scheme is developed to solve this mixed-integer nonlinear problem. Numerical results show that the proposed system could work efficiently and meet users' requirements. In addition, compared with two benchmark schemes, our scheme reduces the failure rate or resource consumption of positioning services by more than 76.2% or 82.7%.
Effective Parallelism for Equation and Jacobian Evaluation in Power Flow Calculation
Authors: Hantao Cui, Fangxing Li, Xin Fang
Date: 2020-11-24 00:00:00

This letter investigates parallelism approaches for equation and Jacobian evaluations in large-scale power flow calculation. Two levels of parallelism are proposed and analyzed: inter-model parallelism, which evaluates models in parallel, and intra-model parallelism, which evaluates calculations within each model in parallel. Parallelism techniques such as multi-threading and single instruction multiple data (SIMD) vectorization are discussed, implemented, and benchmarked as six calculation workflows. Case studies on the 70,000-bus synthetic grid show that equation evaluations can be accelerated by ten times, and the overall Newton power flow advances the state of the art by 20%.
Observability is Sufficient for the Design of Globally Exponentially Convergent State Observers for State-affine Nonlinear Systems
Authors: Lei Wang, Romeo Ortega, Alexei Bobtsov
Date: 2021-08-21 00:00:00

In this paper we are interested in the problem of state observation of state-affine nonlinear systems. Our main contribution is to propose a globally exponentially convergent observer that requires only the necessary assumption of observability of the system. To the best of the authors' knowledge this is the first time such a result is reported in the literature.
Observer Design for Nonlinear Systems with Equivariance
Authors: Robert Mahony, Pieter van Goor, Tarek Hamel
Date: 2021-08-20 00:00:00

Equivariance is a common and natural property of many nonlinear control systems, especially those associated with models of mechatronic and navigation systems. Such systems admit a symmetry, associated with the equivariance, that provides structure enabling the design of robust and high performance observers. A key insight is to pose the observer state to lie in the symmetry group rather than on the system state space. This allows one to define a globally defined intrinsic equivariant error but poses a challenge in defining internal dynamics for the observer. By choosing an equivariant lift of the system dynamics for the observer internal model we show that the error dynamics have a particularly nice form. Applying the methodology of Extended Kalman Filtering (EKF) to the equivariant error state yields the Equivariant Filter (EqF). The geometry of the state-space manifold appears naturally as a curvature modification to the classical EKF Riccati equation. The equivariant filter exploits the symmetry and respects the geometry of an equivariant system model and yields high performance robust filters for a wide range of mechatronic and navigation systems.
Operating Dynamic Reserve Dimensioning Using Probabilistic Forecasts
Authors: Napoleon Costilla-Enriquez, Miguel Ortega-Vazquez, Aidan Tuohy, Amber Motley, Rebecca Webb
Date: 2021-08-20 00:00:00

The rapid integration of variable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against such variability and uncertainty. Traditionally, reserve requirements are determined by rules-of-thumb (static reserve requirements, e.g., NERC Reliability Standards), and more recently, dynamic reserve requirements from tools and methods which are in the adoption process (e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools significantly improve the static rule-of-thumb approaches, they rely exclusively on deterministic data (i.e., best guess only). Consequently, these methods disregard the probabilistic uncertainty thresholds associated with specific days and their weather conditions (i.e., best guess plus probabilistic uncertainty). This work presents practical approaches to determine the operating reserve requirements leveraging the wealth information from probabilistic forecasts. Proposed approaches are validated and tested using actual data from the CAISO system. Results show the benefits in terms of risk reduction of considering the probabilistic forecast information into the dimensioning process of operating reserve requirements.
IQ Photonic Receiver for Coherent Imaging with a Scalable Aperture
Authors: Aroutin Khachaturian, Reza Fatemi, Ali Hajimiri
Date: 2021-08-17 00:00:00

Silicon photonics (SiP) integrated coherent image sensors offer higher sensitivity and improved range-resolution-product compared to direct detection image sensors such as CCD and CMOS devices. Previous generation of SiP coherent imagers suffer from relative optical phase fluctuations between the signal and reference paths, which results in random phase and amplitude fluctuations in the output signal. This limitation negatively impacts the SNR and signal acquisition times. Here we present a coherent imager system that suppresses the optical carrier signal and removes non-idealities from the relative optical path using a photonic in-phase (I) and quadrature (Q) receiver via a $90^\circ$ hybrid detector. Furthermore, we incorporate row-column read-out and row-column addressing schemes to address the electro-optical interconnect density challenge. Our novel row-column read-out architecture for the sensor array requires only $2N$ interconnects for $N^2$ sensors. An $8\times8$ IQ sensor array is presented as a proof-of-concept demonstration with $1.2\times 10^{-5}$ resolution over range accuracy. Free-space FMCW ranging with 250um resolution at 1m distance has been demonstrated using this sensor array.
Local contact inhibition leads to universal principles of cell population growth
Authors: Gregory J. Kimmel, Jeffrey West, Mehdi Damaghi, Alexander R. A. Anderson, Philipp M. Altrock
Date: 2021-08-23 00:00:00

Cancer cell population dynamics often exhibit remarkably replicable, universal laws despite their underlying heterogeneity. Mechanistic explanations of universal cell population growth remain partly unresolved to this day, whereby population feedback between the microscopic and mesoscopic configurations can lead to macroscopic growth laws. We here present a unification under density-dependent birth events via contact inhibition. We consider five classical tumor growth laws: exponential, generalized logistic, Gompertz, radial growth, and fractal growth, which can be seen as manifestations of a single microscopic model. Our theory is substantiated by agent based simulations and can explain growth curve differences in experimental data from in vitro cancer cell population growth. Thus, our framework offers a possible explanation for the large number of mean-field laws that can adequately capture seemingly unrelated cancer or microbial growth dynamics.
Circuit design and integration feasibility of a high-resolution broadband on-chip spectral monitor
Authors: Mehedi Hasan, Gazi Mahamud Hasan, Houman Ghorbani, Mohammad Rad, Peng Liu, Eric Bernier, Trevor Hall
Date: 2021-08-12 00:00:00

Up-to-date network telemetry is the key enabler for resource optimization by a variety of means including capacity scaling, fault recovery, network reconfiguration. Reliable optical performance monitoring in general and specifically the monitoring of the spectral profile of WDM signals in fixed- and flex-grid architecture across the entire C-band remains challenging. This article describes a spectrometer circuit architecture along with an original data processing algorithm that combined can measure the spectrum quantitatively across the entire C-band aiming at 1 GHz resolution bandwidth. The circuit is composed of a scanning ring resonator followed by a parallel arrangement of AWGs with interlaced channel spectra. The comb of ring resonances provides the high resolution and the algorithm creates a virtual tuneable AWG that isolates individual resonances of the comb within the flat pass-band of its synthesized channels. The parallel arrangement of AWGs may be replaced by a time multiplexed multi-input port AWG. The feasibility of a ring resonator functioning over whole C-band is experimentally validated. Full tuning of the comb of resonances over a free spectral range is achieved with a high-resolution bandwidth of 1.30 GHz. Due to its maturity and low loss, CMOS compatible silicon nitride is chosen for integration. Additionally, the whole system demonstration is presented using industry standard simulation tool. The architecture is robust to fabrication process variations owing to its data processing approach.
Mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variants
Authors: Karen Y. Oróstica, Sebastian Contreras, Sebastian B. Mohr, Jonas Dehning, Simon Bauer, David Medina-Ortiz, Emil N. Iftekhar, Karen Mujica, Paulo C. Covarrubias, Soledad Ulloa, Andrés E. Castillo, Ricardo A. Verdugo, Jorge Fernández, Álvaro Olivera-Nappa, Viola Priesemann
Date: 2021-08-23 00:00:00

The emergence of SARS-CoV-2 variants of concern endangers the long-term control of COVID-19, especially in countries with limited genomic surveillance. In this work, we explored genomic drivers of contagion in Chile. We sequenced 3443 SARS-CoV-2 genomes collected between January and July 2021, where the Gamma (P.1), Lambda (C.37), Alpha (B.1.1.7), B.1.1.348, and B.1.1 lineages were predominant. Using a Bayesian model tailored for limited genomic surveillance, we found that Lambda and Gamma variants' reproduction numbers were about 5% and 16% larger than Alpha's, respectively. We observed an overabundance of mutations in the Spike gene, strongly correlated with the variant's transmissibility. Furthermore, the variants' mutational signatures featured a breakpoint concurrent with the beginning of vaccination (mostly CoronaVac, an inactivated virus vaccine), indicating an additional putative selective pressure. Thus, our work provides a reliable method for quantifying novel variants' transmissibility under subsampling (as newly-reported Delta, B.1.617.2) and highlights the importance of continuous genomic surveillance.