pwc-archive/papers-with-abstracts huggingface dataset:
Analysis and Design Considerations for Achieving the Fundamental Limits of Phase Noise in mmWave Oscillators with On-Chip MEMS Resonator
Authors: Abhishek Srivastava, Baibhab Chatterjee, Udit Rawat, Yanbo He, Dana Weinstein, Shreyas Sen
Date: 2020-05-13 00:00:00

Very small electromechanical coupling coefficient in micro-electromechanical systems (MEMS) or acoustic resonators is quite of a concern for oscillator performance, specially at mmWave frequencies. This small coefficient is the manifestation of the small ratio of motional capacitance to static capacitance in the resonators. This work provides a general solution to overcome the problem of relatively high static capacitance at mmWave frequencies and presents analysis and design techniques for achieving extremely low phase noise and a very high figure-of-merit (FoM) in an on-chip MEMS resonator based mmWave oscillator. The proposed analysis and techniques are validated with design and simulation of a 30 GHz oscillator with MEMS resonator having quality factor of 10,000 in 14 nm GF technology. Post layout simulation results show that it achieves a phase noise of -132 dBc/Hz and FoM of 217 dBc/Hz at offset of 1 MHz.
Training Strategies for Autoencoder-based Detection of False Data Injection Attacks
Authors: Chenguang Wang, Kaikai Pan, Simon Tindemans, Peter Palensky
Date: 2020-05-14 00:00:00

The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.
The Model-Specific Markov Embedding Problem for Symmetric Group-Based Models
Authors: Muhammad Ardiyansyah, Dimitra Kosta, Kaie Kubjas
Date: 2020-05-14 00:00:00

We study model embeddability, which is a variation of the famous embedding problem in probability theory, when apart from the requirement that the Markov matrix is the matrix exponential of a rate matrix, we additionally ask that the rate matrix follows the model structure. We provide a characterisation of model embeddable Markov matrices corresponding to symmetric group-based phylogenetic models. In particular, we provide necessary and sufficient conditions in terms of the eigenvalues of symmetric group-based matrices. To showcase our main result on model embeddability, we provide an application to hachimoji models, which are eight-state models for synthetic DNA. Moreover, our main result on model embeddability enables us to compute the volume of the set of model embeddable Markov matrices relative to the volume of other relevant sets of Markov matrices within the model.
Epileptic Seizure Forecasting: Probabilistic seizure-risk assessment and data-fusion
Authors: Nhan Duy Truong, Yikai Yang, Christina Maher, Armin Nikpour, Omid Kavehei
Date: 2020-05-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to a long list of potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a remarkable issue of balancing accuracy and false-alarms. In this paper, we developed a seizure-risk warning system that employs Bayesian convolutional neural network (BCNN) to provide meaningful information to the patient and provide a greater opportunity for him/her to be potentially more in charge of his/her health. We use scalp electroencephalogram (EEG) signals and release information on the certainty of our automatic seizure-risk assessment. In the process, we pave the ground-work towards incorporating auxiliary signals to improve our EEG-based seizure-risk assessment system. Our previous CNN results show an average AUC of 74.65% while we could achieve on an EEG-only BCNN an average AUC of 68.70%. This drop in performance is the cost of providing richer information to the patient at this stage of this research.
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding
Authors: Linfang Wang, Maximilian Stark, Richard D. Wesel, Gerhard Bauch
Date: 2020-05-14 00:00:00
Tasks: Decoder, Quantization

In this paper, we propose a finite-precision decoding method that features the three steps of Reconstruction, Computation, and Quantization (RCQ). Unlike Mutual-Information-Maximization Quantized Belief Propagation (MIM-QBP), RCQ can approximate either belief propagation or Min-Sum decoding. One problem faced by MIM-QBP decoder is that it cannot work well when the fraction of degree-2 variable nodes is large. However, sometimes a large fraction of degree-2 variable nodes is necessary for a fast encoding structure, as seen in the IEEE 802.11 standard and the DVB-S2 standard. In contrast, the proposed RCQ decoder may be applied to any off-the-shelf LDPC code, including those with a large fraction of degree-2 variable nodes.Our simulations show that a 4-bit Min-Sum RCQ decoder delivers frame error rate (FER) performance around 0.1dB of full-precision belief propagation (BP) for the IEEE 802.11 standard LDPC code in the low SNR region.The RCQ decoder actually outperforms full-precision BP in the high SNR region because it overcomes elementary trapping sets that create an error floor under BP decoding. This paper also introduces Hierarchical Dynamic Quantization (HDQ) to design the non-uniform quantizers required by RCQ decoders. HDQ is a low-complexity design technique that is slightly sub-optimal. Simulation results comparing HDQ and an optimal quantizer on the symmetric binary-input memoryless additive white Gaussian noise channel show a loss in mutual information between these two quantizers of less than $10^{-6}$ bits, which is negligible for practical applications.
Dynamic information design
Date: 2020-05-13 00:00:00

We consider the problem of dynamic information design with one sender and one
receiver where the sender observers a private state of the system and takes an
action to send a signal based on its observation to a receiver. Based on this
signal, the receiver takes an action that determines rewards for both the
sender and the receiver and controls the state of the system. In this technical
note, we show that this problem can be considered as a problem of dynamic game
of asymmetric information and its perfect Bayesian equilibrium (PBE) and
Stackelberg equilibrium (SE) can be analyzed using the algorithms presented in
[1], [2] by the same author (among others). We then extend this model when
there is one sender and multiple receivers and provide algorithms to compute a
class of equilibria of this game.
Observed and estimated prevalence of Covid-19 in Italy: Is it possible to estimate the total cases from medical swabs data?
Date: 2020-05-12 00:00:00
Tasks: Time Series Analysis

During the current Covid-19 pandemic in Italy, official data are collected
with medical swabs following a pure convenience criterion which, at least in an
early phase, has privileged the exam of patients showing evident symptoms.
However, there are evidences of a very high proportion of asymptomatic patients
(e. g. Aguilar et al., 2020; Chugthai et al, 2020; Li, et al., 2020; Mizumoto
et al., 2020a, 2020b and Yelin et al., 2020). In this situation, in order to
estimate the real number of infected (and to estimate the lethality rate), it
should be necessary to run a properly designed sample survey through which it
would be possible to calculate the probability of inclusion and hence draw
sound probabilistic inference. Some researchers proposed estimates of the total
prevalence based on various approaches, including epidemiologic models, time
series and the analysis of data collected in countries that faced the epidemic
in earlier time (Brogi et al., 2020). In this paper, we propose to estimate the
prevalence of Covid-19 in Italy by reweighting the available official data
published by the Istituto Superiore di Sanit\`a so as to obtain a more
representative sample of the Italian population. Reweighting is a procedure
commonly used to artificially modify the sample composition so as to obtain a
distribution which is more similar to the population (Valliant et al., 2018).
In this paper, we will use post-stratification of the official data, in order
to derive the weights necessary for reweighting them using age and gender as
post-stratification variables thus obtaining more reliable estimation of
prevalence and lethality.
Sewage Pooling Test for SARS-CoV-2
Authors: Ritam Guha, Anik Sengupta, Ankan Dutta
Date: 2020-05-13 00:00:00

CoVID-19 is currently one of the biggest threats to mankind. To date, it is the reason for infections of over 35 lakhs and the death of over 2 lakh human beings. We propose a procedure to detect CoVID-19 affected localities using a sewage mass testing and pooling mechanism which has gained ground in recent times. The proposed method named Sewage Pooling Algorithm tests wastewater samples from sewage systems to pinpoint the regions which are affected by maximum chances of the virus spread. The algorithm also uses a priority-based backtracking procedure to perform testing in sewage links depending on the probability of infection in the sub-areas. For places with very rare CoVID cases, we present a gradient-based search method to prune those areas. The proposed method has less human intervention and increases the effective tests/million people over current in-place methods.
Anisotropy links cell shapes to tissue flow during convergent extension
Authors: Xun Wang, Matthias Merkel, Leo B. Sutter, Gonca Erdemci-Tandogan, M. Lisa Manning, Karen E. Kasza
Date: 2020-05-14 00:00:00

Within developing embryos, tissues flow and reorganize dramatically on timescales as short as minutes. This includes epithelial tissues, which often narrow and elongate in convergent extension movements due to anisotropies in external forces or in internal cell-generated forces. However, the mechanisms that allow or prevent tissue reorganization, especially in the presence of strongly anisotropic forces, remain unclear. We study this question in the converging and extending Drosophila germband epithelium, which displays planar polarized myosin II and experiences anisotropic forces from neighboring tissues, and we show that in contrast to isotropic tissues, cell shape alone is not sufficient to predict the onset of rapid cell rearrangement. From theoretical considerations and vertex model simulations, we predict that in anisotropic tissues two experimentally accessible metrics of cell patterns, the cell shape index and a cell alignment index, are required to determine whether an anisotropic tissue is in a solid-like or fluid-like state. We show that changes in cell shape and alignment over time in the Drosophila germband predict the onset of rapid cell rearrangement in both wild-type and snail twist mutant embryos, where our theoretical prediction is further improved when we also account for cell packing disorder. These findings suggest that convergent extension is associated with a transition to more fluid-like tissue behavior, which may help accommodate tissue shape changes during rapid developmental events.
Robust Safety-Critical Control for Dynamic Robotics
Authors: Quan Nguyen, Koushil Sreenath
Date: 2020-05-14 00:00:00

We present a novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the presence of model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on dynamic walking of a five-link planar bipedal robot subject to contact force constraints as well as safety-critical precise foot placements on stepping stones, all while subject to model uncertainty. We conduct preliminary experimental validation of the proposed controller on a rectilinear spring-cart system under different types of model uncertainty and perturbations.
Securing Mobile IoT with Unmanned Aerial Systems
Authors: Aly Sabri Abdalla, Bodong Shang, Vuk Marojevic, Lingjia Liu
Date: 2020-05-15 00:00:00

The Internet of Things (IoT) will soon be omnipresent and billions of sensors and actuators will support our industries and well-being. IoT devices are embedded systems that are connected using wireless technology for most of the cases. The availability of the wireless network serving the IoT, the privacy, integrity, and trustworthiness of the data are of critical importance, since IoT will drive businesses and personal decisions. This paper proposes a new approach in the wireless security domain that leverages advanced wireless technology and the emergence of the unmanned aerial system or vehicle (UAS or UAV). We consider the problem of eavesdropping and analyze how UAVs can aid in reducing, or overcoming this threat in the mobile IoT context. The results show that huge improvements in terms of channel secrecy rate can be achieved when UAVs assist base stations for relaying the information to the desired IoT nodes. Our approach is technology agnostic and can be expanded to address other communications security aspects.
Context Detection for Advanced Self-Aware Navigation using Smartphone Sensors
Authors: Han Gao, Paul D. Groves
Date: 2020-05-15 00:00:00

Navigation and positioning systems dependent on both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning and the behaviour can contribute additional information to the navigation solution. In order to operate across different contexts, a context-adaptive navigation solution introduces an element of self-awareness by detecting the operating context and configuring the positioning system accordingly. This paper presents the detection of both environmental and behavioural contexts as a whole, building the foundation of a context-adaptive navigation system. Behavioural contexts are classified using measurements from accelerometers, gyroscopes, magnetometers and the barometer by supervised machine learning algorithms, yielding an overall 95% classification accuracy. A connectivity dependent filter is then implemented to improve the behavioural detection results. Environmental contexts are detected from GNSS measurements. They are classified into indoor, intermediate and outdoor categories using a probabilistic support vector machine (SVM), followed by a hidden Markov model (HMM) used for time-domain filtering. As there will never be completely reliable context detection, the paper also shows how environment and behaviour association can contribute to reducing the chances of the context determination algorithms selecting an incorrect context. Finally, the proposed context-determination algorithms are tested in a series of multi-context scenarios.
Design and implementation of the net zero displacement filter for the synthesis of a mechanical shock signal under specified shock response spectrum
Authors: Y. Yan, Q. M. Li
Date: 2020-05-15 00:00:00

Electronic and optical products are vulnerable under mechanical shock environment. Designed products need to be validated by shock testing and/or numerical simulation, using representative acceleration-time history signals. However, specifications derived from measurements are normally given in terms of shock response spectrum (SRS) without a corresponding time history signal, and therefore, there is a need to synthesis acceleration-time histories from a given SRS specification. This paper proposed a net zero displacement filter and a realistic time history synthesis method. By scaling a relevant field measurement, acceleration-time histories can be synthesized, which can meet the net zero displacement constrain and a given SRS specification within $\pm$3 dB margin.
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Authors: Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
Date: 2020-05-15 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser. While PnP algorithms are well understood for denoisers performing maximum a posteriori probability (MAP) estimation, they have not been analyzed for the minimum mean squared error (MMSE) denoisers. This letter addresses this gap by establishing the first theoretical convergence result for the iterative shrinkage/thresholding algorithm (ISTA) variant of PnP for MMSE denoisers. We show that the iterates produced by PnP-ISTA with an MMSE denoiser converge to a stationary point of some global cost function. We validate our analysis on sparse signal recovery in compressive sensing by comparing two types of denoisers, namely the exact MMSE denoiser and the approximate MMSE denoiser obtained by training a deep neural net.
Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach
Authors: Weijie Yuan, Fan Liu, Christos Masouros, Jinhong Yuan, Derrick Wing Kwan Ng, Nuria Gonzalez-Prelcic
Date: 2020-05-15 00:00:00
Tasks: Joint Radar-Communication

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side units estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links.}With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle-based methods.
Optical image decomposition and noise filtering based on Laguerre-Gaussian modes
Date: 2020-05-15 00:00:00

We propose and experimentally demonstrate an efficient image decomposition in
the Laguerre-Gaussian (LG) domain. By developing an advanced computing method,
the sampling points are much fewer than those in the existing methods, which
can significantly improve the calculation efficiency. The beam waist, azimuthal
and radial truncation orders of the LG modes are optimized depending on the
image information to be restored. In the experiment, we decompose an image by
using about 30000 LG modes and realize a high-fidelity reconstruction.
Furthermore, we show image noise reduction through LG domain filtering. Our
results open a door for LG-mode based image processing.
Caiman crocodilus (Spectacled caiman). Opportunistic foraging
Authors: Paul B. C. Grant, Todd R. Lewis, Thomas C. LaDuke, Colin Ryall
Date: 2020-05-18 00:00:00

We document opportunistic foraging behavior by Caiman crocodilus in a post-inundation forest at Estac\'ion Biolog\'ica Ca\~no Palma, Costa Rica.
The fast and the slow axonal transport: a unified approach based on cargo and molecular motors coupled dynamics
Authors: Alexandre Y. C. Cho, Victor R. C. M. Roque, Carla Goldman
Date: 2020-05-15 00:00:00

The origins of the large differences observed to the rates with which the diverse particles are conveyed along axonal microtubules are still a matter of debate in the literature. There is evidence that certain neurodegenerative diseases may be triggered by disturbances to the related transport processes. Motivated by this, we employ a model to investigate the mobility properties of certain cargoes which dynamics are coupled with that of molecular motors on crowded microtubules. For certain initial and boundary conditions, we use the method of characteristics to resolve perturbatively the pair of equations of the Burgers type resulting from a mean-field approach to the original microscopic stochastic model. Extensions to the non-perturbative limits are explored numerically. In this context, we were able to figure out conditions under which cargos average velocities may differ up to orders of magnitude just by changing the number of motors on the considered track. We then discuss possibilities to connect these theoretical predictions with available experimental data about axon transport.
Parameters of Profitability: Evidence From Conventional and Islamic Banks of Bangladesh
Authors: K. M. Golam Muhiuddin, Nusrat Jahan
Date: 2020-05-15 00:00:00

This paper evaluates the commercial banks of Bangladesh in terms of profitability dimension of performance and also examines the impact of selected determinants and banking system on this dimension of performance. Evaluation of trend in profitability of listed commercial banks of Bangladesh reveals that, on an average, profitability is exhibiting a decreasing trend over the selected period; however, the profitability performance of Islamic banks remained rather high compared to Conventional banks. Profitability measured by Return on Asset is found to be significantly affected by the bank-specific factors, industry-specific factor and the banking system. However, macro-economic factors evidently have no significant impact on profitability of commercial banks of Bangladesh.
Massive MIMO Channel Estimation with Low-Resolution Spatial Sigma-Delta ADCs
Authors: Shilpa Rao, Gonzalo Seco-Granados, Hessam Pirzadeh, Josef A. Nossek, A. Lee Swindlehurst
Date: 2020-05-15 00:00:00
Tasks: Quantization

We consider channel estimation for an uplink massive multiple-input multiple-output (MIMO) system where the base station (BS) uses an array with low-resolution (1-2 bit) analog-to-digital converters and a spatial Sigma-Delta ($\Sigma\Delta$) architecture to shape the quantization noise away from users in some angular sector. We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. We also analyze the uplink achievable rate with maximal ratio combining (MRC), zero-forcing (ZF), and LMMSE receivers and provide a lower bound for the achievable rate with the MRC receiver. Numerical results show superior channel estimation and sum spectral efficiency performance using the $\Sigma\Delta$ architecture compared to conventional 1- or 2-bit quantized massive MIMO systems.