pwc-archive/papers-with-abstracts huggingface dataset:
The INESC-ID Multi-Modal System for the ADReSS 2020 Challenge
Date: 2020-05-29 00:00:00
Tasks: Classification, Sentence, Sentence Embeddings

This paper describes a multi-modal approach for the automatic detection of
Alzheimer's disease proposed in the context of the INESC-ID Human Language
Technology Laboratory participation in the ADReSS 2020 challenge. Our
classification framework takes advantage of both acoustic and textual feature
embeddings, which are extracted independently and later combined. Speech
signals are encoded into acoustic features using DNN speaker embeddings
extracted from pre-trained models. For textual input, contextual embedding
vectors are first extracted using an English Bert model and then used either to
directly compute sentence embeddings or to feed a bidirectional LSTM-RNNs with
attention. Finally, an SVM classifier with linear kernel is used for the
individual evaluation of the three systems. Our best system, based on the
combination of linguistic and acoustic information, attained a classification
accuracy of 81.25%. Results have shown the importance of linguistic features in
the classification of Alzheimer's Disease, which outperforms the acoustic ones
in terms of accuracy. Early stage features fusion did not provide additional
improvements, confirming that the discriminant ability conveyed by speech in
this case is smooth out by linguistic data.
Mutations strengthened SARS-CoV-2 infectivity
Date: 2020-05-27 00:00:00

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a
major concern in coronavirus disease 2019 (COVID-19) prevention and economic
reopening. However, rigorous determination of SARS-COV-2 infectivity is
essentially impossible owing to its continuous evolution with over 13752 single
nucleotide polymorphisms (SNP) variants in six different subtypes. We develop
an advanced machine learning algorithm based on the algebraic topology to
quantitatively evaluate the binding affinity changes of SARS-CoV-2 spike
glycoprotein (S protein) and host angiotensin-converting enzyme 2 (ACE2)
receptor following the mutations. Based on mutation-induced binding affinity
changes, we reveal that five out of six SARS-CoV-2 subtypes have become either
moderately or slightly more infectious, while one subtype has weakened its
infectivity. We find that SARS-CoV-2 is slightly more infectious than SARS-CoV
according to computed S protein-ACE2 binding affinity changes. Based on a
systematic evaluation of all possible 3686 future mutations on the S protein
receptor-binding domain (RBD), we show that most likely future mutations will
make SARS-CoV-2 more infectious. Combining sequence alignment, probability
analysis, and binding affinity calculation, we predict that a few residues on
the receptor-binding motif (RBM), i.e., 452, 489, 500, 501, and 505, have very
high chances to mutate into significantly more infectious COVID-19 strains.
Can Testing Ease Social Distancing Measures? Future Evolution of COVID-19 in NYC
Date: 2020-05-27 00:00:00

The "New York State on Pause" executive order came into effect on March 22
with the goal of ensuring adequate social distancing to alleviate the spread of
COVID-19. Pause will remain effective in New York City in some form until early
June. We use a compartmentalized model to study the effects of testing capacity
and social distancing measures on the evolution of the pandemic in the
"post-Pause" period in the City. We find that testing capacity must increase
dramatically if it is to counterbalance even relatively small relaxations in
social distancing measures in the immediate post-Pause period. In particular,
if the City performs 20,000 tests per day and relaxes the social distancing
measures to the pre-Pause norms, then the total number of deaths by the end of
September can reach 250,000. By keeping the social distancing measures to
somewhere halfway between the pre- and in-Pause norms and performing 100,000
tests per day, the total number of deaths by the end of September can be kept
at around 27,000. Going back to the pre-Pause social distancing norms quickly
must be accompanied by an exorbitant testing capacity, if one is to suppress
excessive deaths. If the City is to go back to the "pre-Pause" social
distancing norms in the immediate post-Pause period and keep the total number
of deaths by the end of September at around 35,000, then it should be
performing 500,000 tests per day. Our findings have important implications on
the magnitude of the testing capacity the City needs as it relaxes the social
distancing measures to reopen its economy.
Modelling oral adrenal cortisol support
Authors: David J. Smith, Alessandro Prete, Angela E. Taylor, Niki Karavitaki, Wiebke Arlt
Date: 2020-05-29 00:00:00

A simplified mathematical model of oral hydrocortisone delivery in adrenal insufficiency is described; the model is based on three components (gastric hydrocortisone, free serum cortisol and bound serum cortisol) and is formulated in terms of linear kinetics, taking into account the dynamics of glucocorticoid-protein binding. Motivated by the need to optimise cortisol replacement in the situations of COVID-19 infection, the model is fitted to recently-published data on 50 mg dosing and earlier data on 10 mg dosing. The fitted model is used to predict typical responses to standard dosing regimes, which involve a larger dose in the morning and 1 or 2 smaller doses later in the day, and the same regimes with doses doubled. In all cases there is a circadian-like response, with early morning nadir. The model is also used to consider an alternative dosing strategy based on four equal and equally-spaced doses of 10, 20 or 30 mg per 24 h, resulting in a more even response resembling a response to sustained inflammatory stress.
Estimating Hidden Asymptomatics, Herd Immunity Threshold and Lockdown Effects using a COVID-19 Specific Model
Date: 2020-05-29 00:00:00

A quantitative COVID-19 model that incorporates hidden asymptomatic patients
is developed, and an analytic solution in parametric form is given. The model
incorporates the impact of lockdown and resulting spatial migration of
population due to announcement of lockdown. A method is presented for
estimating the model parameters from real-world data. It is shown that increase
of infections slows down and herd immunity is achieved when symptomatic
patients are 4-6\% of the population for the European countries we studied,
when the total infected fraction is between 50-56 \%. Finally, a method for
estimating the number of asymptomatic patients, who have been the key hidden
link in the spread of the infections, is presented.
PointNet on FPGA for Real-Time LiDAR Point Cloud Processing
Authors: Lin Bai, Yecheng Lyu, Xin Xu, Xinming Huang
Date: 2020-05-29 00:00:00
Tasks: Autonomous Vehicles, Segmentation

LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization. This paper presents an FPGA-based deep learning platform for real-time point cloud processing targeted on autonomous vehicles. The software driver for the Velodyne LiDAR sensor is modified and moved into the on-chip processor system, while the programmable logic is designed as a customized hardware accelerator. As the state-of-art deep learning algorithm for point cloud processing, PointNet is successfully implemented on the proposed FPGA platform. Targeted on a Xilinx Zynq UltraScale+ MPSoC ZCU104 development board, the FPGA implementations of PointNet achieve the computing performance of 182.1 GOPS and 280.0 GOPS for classification and segmentation respectively. The proposed design can support an input up to 4096 points per frame. The processing time is 19.8 ms for classification and 34.6 ms for segmentation, which meets the real-time requirement for most of the existing LiDAR sensors.
A Unified Hardware Architecture for Convolutions and Deconvolutions in CNN
Authors: Lin Bai, Yecheng Lyu, Xinming Huang
Date: 2020-05-29 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this paper, a scalable neural network hardware architecture for image segmentation is proposed. By sharing the same computing resources, both convolution and deconvolution operations are handled by the same process element array. In addition, access to on-chip and off-chip memories is optimized to alleviate the burden introduced by partial sum. As an example, SegNet-Basic has been implemented using the proposed unified architecture by targeting on Xilinx ZC706 FPGA, which achieves the performance of 151.5 GOPS and 94.3 GOPS for convolution and deconvolution respectively. This unified convolution/deconvolution design is applicable to other CNNs with deconvolution.
Improving Mammography Malignancy Segmentation by Designing the Training Process
Date: 2020-05-29 00:00:00
Proceeding: MIDL 2019 7
Tasks: Segmentation, Transfer Learning

We work on the breast imaging malignancy segmentation task while focusing on
the training process instead of network complexity. We designed a training
process based on a modified U-Net, increasing the overall segmentation
performances by using both, benign and malignant data for training. Our
approach makes use of only a small amount of annotated data and relies on
transfer learning from a self-supervised reconstruction task, and favors
explainability.
The impacts of asymmetry on modeling and forecasting realized volatility in Japanese stock markets
Authors: Daiki Maki, Yasushi Ota
Date: 2020-05-30 00:00:00

This study investigates the impacts of asymmetry on the modeling and forecasting of realized volatility in the Japanese futures and spot stock markets. We employ heterogeneous autoregressive (HAR) models allowing for three types of asymmetry: positive and negative realized semivariance (RSV), asymmetric jumps, and leverage effects. The estimation results show that leverage effects clearly influence the modeling of realized volatility models. Leverage effects exist for both the spot and futures markets in the Nikkei 225. Although realized semivariance aids better modeling, the estimations of RSV models depend on whether these models have leverage effects. Asymmetric jump components do not have a clear influence on realized volatility models. While leverage effects and realized semivariance also improve the out-of-sample forecast performance of volatility models, asymmetric jumps are not useful for predictive ability. The empirical results of this study indicate that asymmetric information, in particular, leverage effects and realized semivariance, yield better modeling and more accurate forecast performance. Accordingly, asymmetric information should be included when we model and forecast the realized volatility of Japanese stock markets.
A Novel RF-assisted-Strobe System for Unobtrusive Vibration Detection of Machine Parts
Authors: Dibyendu Roy, Arijit Sinharay, Brojeshwar Bhowmick, Raj Rakshit, Tapas Chakravarty, Arpan Pal
Date: 2020-05-30 00:00:00

In this paper, we propose a novel non-contact vibration measurement system that is competent in estimating linear and/or rotational motions of machine parts. The technique combines microwave radar, standard camera, and optical strobe to capture vibrational or rotational motions in a relatively fast and affordable manner when compared to the current technologies. In particular, the proposed technique is capable of not only measuring common vibrational parameters (e.g. frequency, motor rpm, etc.) but also provides spatial information of the vibrational sources so that the origin of each vibrational point can be identified accurately. Furthermore, it can also capture the wobbling motion of the rotating shafts. Thus, the proposed method can find immense applications in preventive maintenance across various industries where heavy machinery needs to be monitored unobtrusively or there is a requirement for non-contact multi-point vibration measurement for any machine inspection applications.
Climate Monitoring using Internet of X-Things
Authors: Nasir Saeed, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
Date: 2020-05-30 00:00:00

Global climate change is significantly affecting the life on planet Earth. Predicting timely changes in the climate is a big challenge and requires great attention from the scientific community. Research now suggests that using the internet of X-things (X-IoT) helps in monitoring global climate change.
The impact of COVID-19 on the UK fresh food supply chain
Date: 2020-05-30 00:00:00

The resilience of the food supply chain is a matter of critical importance,
both for national security and broader societal well bring. COVID19 has
presented a test to the current system, as well as means by which to explore
whether the UK's food supply chain will be resilient to future disruptions. In
the face of a growing need to ensure that food supply is more environmentally
sustainable and socially just, COVOD19 also represents an opportunity to
consider the ability of the system to innovative, and its capacity for change.
The purpose of this case based study is to explore the response and resilience
of the UK fruit and vegetable food supply chain to COVID19, and to assess this
empirical evidence in the context of a resilience framework based on the
adaptive cycle. To achieve this we reviewed secondary data associated with
changes to retail demand, conducted interviews with 23 organisations associated
with supply to this market, and conducted four video workshops with 80
organisations representing half of the UK fresh produce community. The results
highlight that, despite significant disruption, the retail dominated fresh food
supply chain has demonstrated a high degree of resilience. In the context of
the adaptive cycle, the system has shown signs of being stuck in a rigidity
trap, as yet unable to exploit more radical innovations that may also assist in
addressing other drivers for change. This has highlighted the significant role
that innovation and R&D communities will need to play in enabling the supply
chain to imagine and implement alternative future states post COVID.
Dinucleotide repeats in coronavirus SARS-CoV-2 genome: evolutionary implications
Authors: Changchuan Yin
Date: 2020-05-30 00:00:00
Tasks: Diagnostic

The ongoing global pandemic of infection disease COVID-19 caused by the 2019 novel coronavirus (SARS-COV-2, formerly 2019-nCoV) presents critical threats to public health and the economy since it was identified in China, December 2019. The genome of SARS-CoV-2 had been sequenced and structurally annotated, yet little is known of the intrinsic organization and evolution of the genome. To this end, we present a mathematical method for the genomic spectrum, a kind of barcode, of SARS-CoV-2 and common human coronaviruses. The genomic spectrum is constructed according to the periodic distributions of nucleotides, and therefore reflects the unique characteristics of the genome. The results demonstrate that coronavirus SARS-CoV-2 exhibits dinucleotide TT islands in the non-structural proteins 3, 4, 5, and 6. Further analysis of the dinucleotide regions suggests that the dinucleotide repeats are increased during evolution and may confer the evolutionary fitness of the virus. The special dinucleotide regions in the SARS-CoV-2 genome identified in this study may become diagnostic and pharmaceutical targets in monitoring and curing the COVID-19 disease.
When to sell an asset amid anxiety about drawdowns
Date: 2020-05-30 00:00:00
Tasks: Decision Making

We consider risk averse investors with different levels of anxiety about
asset price drawdowns. The latter is defined as the distance of the current
price away from its best performance since inception. These drawdowns can
increase either continuously or by jumps, and will contribute towards the
investor's overall impatience when breaching the investor's private tolerance
level. We investigate the unusual reactions of investors when aiming to sell an
asset under such adverse market conditions. Mathematically, we study the
optimal stopping of the utility of an asset sale with a random discounting that
captures the investor's overall impatience. The random discounting is given by
the cumulative amount of time spent by the drawdowns in an undesirable high
region, fine tuned by the investor's personal tolerance and anxiety about
drawdowns. We prove that in addition to the traditional take-profit sales, the
real-life employed stop-loss orders and trailing stops may become part of the
optimal selling strategy, depending on different personal characteristics. This
paper thus provides insights on the effect of anxiety and its distinction with
traditional risk aversion on decision making.
"Closed Proportional-Integral-Derivative-Loop Model" Following Control
Authors: Oluwasegun Ayokunle Somefun, Kayode Akingbade, Folasade Dahunsi
Date: 2020-05-30 00:00:00
Tasks: model

The proportional-integral-derivative (PID) control law is often overlooked as a computational imitation of the critic control in human decision. This paper provides a formulation to remedy this problem. Further, based on the characteristic settling-behaviour of dynamical systems, the "closed PID-loop model" following control (CPLMFC) method is introduced for automatic PID design. Also, a method for closed-loop settling-time identification is provided. The CPLMFC algorithm and some recommended guidelines are given for setting the critic weights of the PID. Finally, two representative case-studies are simulated. Both the theoretical results and simulation results (via performance indices) illustrate that the CPLMFC can guarantee both accurate and stable closed-loop adaptive PID control performance in real-time
Control Design for Risk-Based Signal Temporal Logic Specifications
Date: 2020-05-30 00:00:00
Tasks: Model Predictive Control

We present a general framework for risk semantics on Signal Temporal Logic
(STL) specifications for stochastic dynamical systems using axiomatic risk
theory. We show that under our recursive risk semantics, risk constraints on
STL formulas can be expressed in terms of risk constraints on atomic
predicates. We then show how this allows a (stochastic) STL risk constraint to
be transformed into a risk-tightened deterministic STL constraint on a related
deterministic nominal system, enabling the application of existing STL methods.
For affine predicate functions and a (coherent) Distributionally Robust Value
at Risk measure, we show how risk constraints on atomic predicates can be
reformulated as tightened deterministic affine constraints. We demonstrate the
framework using a Model Predictive Control (MPC) design with an STL risk
constraint.
Statistical Decision Properties of Imprecise Trials Assessing COVID-19 Drugs
Date: 2020-05-30 00:00:00
Tasks: Articles

As the COVID-19 pandemic progresses, researchers are reporting findings of
randomized trials comparing standard care with care augmented by experimental
drugs. The trials have small sample sizes, so estimates of treatment effects
are imprecise. Seeing imprecision, clinicians reading research articles may
find it difficult to decide when to treat patients with experimental drugs.
Whatever decision criterion one uses, there is always some probability that
random variation in trial outcomes will lead to prescribing sub-optimal
treatments. A conventional practice when comparing standard care and an
innovation is to choose the innovation only if the estimated treatment effect
is positive and statistically significant. This practice defers to standard
care as the status quo. To evaluate decision criteria, we use the concept of
near-optimality, which jointly considers the probability and magnitude of
decision errors. An appealing decision criterion from this perspective is the
empirical success rule, which chooses the treatment with the highest observed
average patient outcome in the trial. Considering the design of recent and
ongoing COVID-19 trials, we show that the empirical success rule yields
treatment results that are much closer to optimal than those generated by
prevailing decision criteria based on hypothesis tests.
A Finite Population Destroys a Traveling Wave in Spatial Replicator Dynamics
Authors: Christopher Griffin, Riley Mummah, Russ deForest
Date: 2020-05-31 00:00:00

We derive both the finite and infinite population spatial replicator dynamics as the fluid limit of a stochastic cellular automaton. The infinite population spatial replicator is identical to the model used by Vickers and our derivation justifies the addition of a diffusion to the replicator. The finite population form generalizes the results by Durett and Levin on finite spatial replicator games. We study the differences in the two equations as they pertain to a one-dimensional rock-paper-scissors game.
A Systematic Review of Mutations Associated with Isoniazid Resistance Points to Lower Diagnostic Sensitivity for Common Mutations and Increased Incidence of Uncommon Mutations in Clinical Strains of Mycobacterium tuberculosis
Authors: Siavash Valafar
Date: 2020-05-31 00:00:00
Tasks: Articles, Diagnostic

Molecular testing is rapidly becoming integral to the global tuberculosis (TB) control effort. Uncommon mechanisms of resistance can escape detection by these platforms and lead to the development of Multi-Drug Resistant (MDR) strains. This article is a systematic review of published articles that reported isoniazid (INH) resistance-conferring mutations between September-2013 and December-2019. The aims were to catalogue mutations associated with INH resistance, estimate their global prevalence and co-occurrence, and their utility in molecular diagnostics. The genes commonly associated with INH resistance, katG, inhA, fabG1, and the intergenic region oxyR-ahpC were considered in this review. In total, 52 articles were included describing 5,632 INHR clinical isolates from 31 countries. The three most frequently mutated loci continue to be katG315 (4,100), inhA-15 (786), and inhA-8 (105). However, the diagnostic value of inhA-8 is far lower than previously thought, only appearing in 25 (0.4%) INHR isolates that lacked a mutation at the first two loci. Importantly, of the four katG loci recommended by the previous systematic review for diagnostics, only katG315 was observed in our INHR isolates. This indicates continued evolution and regional differences in INH resistance. We have identified 58 loci (common to both systematic reviews) in three genomic regions as a reliable basis for molecular diagnostics. We also report 49 new loci associated with INH resistance. Including all observed mutations provides a cumulative sensitivity of 85.1%. The most disconcerting is the remaining 14.9% of isolates that harbor an unknown mechanism of resistance, will escape molecular detection, and likely convert to MDR-TB, further complicating treatment. Integrating the information cataloged in this and other similar studies into current diagnostic tools is essential for combating the emergence of MDR-TB.
SimRIS Channel Simulator for Reconfigurable Intelligent Surface-Empowered Communication Systems
Authors: E. Basar, I. Yildirim
Date: 2020-05-31 00:00:00

Reconfigurable intelligent surface (RIS)-assisted communication appears as one of the potential enablers for sixth generation (6G) wireless networks by providing a new way to optimize the communication system performance. This paper aims to fill an important gap in the open literature by providing an accurate, source, and widely applicable RIS channel model for mmWave frequencies. Our model is not only applicable in various indoor and outdoor environments but also includes the physical aspects of wireless propagation in the presence of an RIS as well as various practical 5G channel modeling issues. The open-source and comprehensive SimRIS Channel Simulator is also introduced in this paper to be used in computer simulations of RIS-assisted communication systems.