pwc-archive/papers-with-abstracts huggingface dataset:
Model-based Randomness Monitor for Stealthy Sensor Attacks
Date: 2020-05-16 00:00:00
Tasks: model

Malicious attacks on modern autonomous cyber-physical systems (CPSs) can
leverage information about the system dynamics and noise characteristics to
hide while hijacking the system toward undesired states. Given attacks
attempting to hide within the system noise profile to remain undetected, an
attacker with the intent to hijack a system will alter sensor measurements,
contradicting with what is expected by the system's model. To deal with this
problem, in this paper we present a framework to detect non-randomness in
sensor measurements on CPSs under the effect of sensor attacks. Specifically,
we propose a run-time monitor that leverages two statistical tests, the
Wilcoxon Signed-Rank test and Serial Independence Runs test to detect
inconsistent patterns in the measurement data. For the proposed statistical
tests we provide formal guarantees and bounds for attack detection. We validate
our approach through simulations and experiments on an unmanned ground vehicle
(UGV) under stealthy attacks and compare our framework with other anomaly
detectors.
Identification of Repurposable Drugs and Adverse Drug Reactions for Various Courses of COVID-19 Based on Single-Cell RNA Sequencing Data
Date: 2020-05-16 00:00:00

Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. There is no specific drug for COVID-19, highlighting the urgent need for the development of effective therapeutics. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating the gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from mild and severe COVID-19 patients. We identified 281 FDA-approved drugs that have the potential to be effective against SARS-CoV-2 infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus. In conclusion, we have identified a list of repurposable anti-SARS-CoV-2 drugs using a systems biology approach.
Remote State Estimation with Smart Sensors over Markov Fading Channels
Authors: Wanchun Liu, Daniel E. Quevedo, Yonghui Li, Karl Henrik Johansson, Branka Vucetic
Date: 2020-05-16 00:00:00
Tasks: State Estimation

We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LTI) systems. A smart sensor forwards its local state estimate to a remote estimator over a time-correlated $M$-state Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state. We establish a necessary and sufficient condition for mean-square stability of the remote estimation error covariance as $\rho^2(\mathbf{A})\rho(\mathbf{DM})<1$, where $\rho(\cdot)$ denotes the spectral radius, $\mathbf{A}$ is the state transition matrix of the LTI system, $\mathbf{D}$ is a diagonal matrix containing the packet drop probabilities in different channel states, and $\mathbf{M}$ is the transition probability matrix of the Markov channel states. To derive this result, we propose a novel estimation-cycle based approach, and provide new element-wise bounds of matrix powers. The stability condition is verified by numerical results, and is shown more effective than existing sufficient conditions in the literature. We observe that the stability region in terms of the packet drop probabilities in different channel states can either be convex or concave depending on the transition probability matrix $\mathbf{M}$. Our numerical results suggest that the stability conditions for remote estimation may coincide for setups with a smart sensor and with a conventional one (which sends raw measurements to the remote estimator), though the smart sensor setup achieves a better estimation performance.
Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm
Authors: Kevin D. Smith, Saber Jafarpour, Ananthram Swami, Francesco Bullo
Date: 2020-05-16 00:00:00
Tasks: Management

Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology. However, the standard approach to topology mapping--namely, active probing with traceroutes--relies on cooperation from increasingly non-cooperative routers, leading to missing information. Network tomography, which uses end-to-end measurements of additive link metrics (like delays or log packet loss rates) across monitor paths, is a possible remedy. Network tomography does not require that routers cooperate with traceroute probes, and it has already been used to infer the structure of multicast trees. This paper goes a step further. We provide a tomographic method to infer the underlying routing topology of an arbitrary set of monitor paths using the joint distribution of end-to-end measurements, without making any assumptions on routing behavior. Our approach, called the M\"obius Inference Algorithm (MIA), uses cumulants of this distribution to quantify high-order interactions among monitor paths, and it applies M\"obius inversion to "disentangle" these interactions. In addition to MIA, we provide a more practical variant called Sparse M\"obius Inference, which uses various sparsity heuristics to reduce the number and order of cumulants required to be estimated. We show the viability of our approach using synthetic case studies based on real-world ISP topologies.
Low-complexity and Low-overhead Receiver for OTFS via Large-scale Antenna Array
Authors: Yaru Shan, Fanggang Wang
Date: 2020-05-16 00:00:00

Orthogonal time frequency space (OTFS) is a modulation technique that is dedicated to the high-speed mobility scenario. However, its transmission involves a two-dimensional convolution of the symbols of interest and the multipath fading channel, and it complicates the equalization. In addition to the high-complexity issue, the existing pilot pattern to estimate the unknown channel accurately requires large overhead to avoid the pilot being contaminated, which is spectrally inefficient. In this paper, we propose a receiver approach by the marriage of the OTFS and a large-scale antenna array, which allows low-complexity detection and low-overhead pilot pattern design. First, the received signal from each path of the multipath fading channel is identified by a high-resolution receive beamformer facilitated by a large-scale antenna array. Then the identified signal from each angle in the delay-Doppler domain reduces to a flat-faded signal, which can be simply equalized using the channel information estimated by our pilot pattern. We further provide the estimator of the channel fading and the rotations of delay and Doppler. With these estimates, the symbols of interest can be recovered, and then, the signals from all angles of arrival are combined as different diversity versions. In addition, our pilot pattern with only around 25% overhead of the existing pilot pattern ensures the same protection of pilot pollution. Eventually, the efficiency, the reliability, and the low complexity of the proposed receiver approach are further validated by the numerical results.
In silico ADMET and molecular docking study on searching potential inhibitors from limonoids and triterpenoids for COVID-19
Authors: Seshu Vardhan, Suban K Sahoo
Date: 2020-05-16 00:00:00
Tasks: Molecular Docking

Virtual screening of phytochemicals was performed through molecular docking, simulation, in silico ADMET and drug-likeness prediction to identify the potential hits that can inhibit the effects of SARS-CoV-2. Considering the published literature on medicinal importance, total 154 phytochemicals with analogous structure from limonoids and triterpenoids were selected to search potential inhibitors for the five therapeutic protein targets of SARS-CoV-2, i.e., 3CLpro (main protease), PLpro (papain-like protease), SGp-RBD (spike glycoprotein-receptor binding domain), RdRp (RNA dependent RNA polymerase) and ACE2 (angiotensin-converting enzyme 2). The in silico computational results revealed that the phytochemicals such as glycyrrhizic acid, limonin, 7-deacetyl-7-benzoylgedunin, maslinic acid, corosolic acid, obacunone and ursolic acid were found to be effective against the target proteins of SARS-CoV-2. The protein-ligand interaction study revealed that these phytochemicals bind with the amino acid residues at the active site of the target proteins. Therefore, the core structure of these potential hits can be used for further lead optimization to design drugs for SARS-CoV-2. Also, the medicinal plants containing these phytochemicals like licorice, neem, tulsi, citrus and olives can be used to formulate suitable therapeutic approaches in traditional medicines.
Parameter estimation of default portfolios using the Merton model and Phase transition
Authors: Masato Hisakado, Shintaro Mori
Date: 2020-05-16 00:00:00
Tasks: parameter estimation

We discuss the parameter estimation of the probability of default (PD), the correlation between the obligors, and a phase transition. In our previous work, we studied the problem using the beta-binomial distribution. A non-equilibrium phase transition with an order parameter occurs when the temporal correlation decays by power law. In this article, we adopt the Merton model, which uses an asset correlation as the default correlation, and find that a phase transition occurs when the temporal correlation decays by power law. When the power index is less than one, the PD estimator converges slowly. Thus, it is difficult to estimate PD with limited historical data. Conversely, when the power index is greater than one, the convergence speed is inversely proportional to the number of samples. We investigate the empirical default data history of several rating agencies. The estimated power index is in the slow convergence range when we use long history data. This suggests that PD could have a long memory and that it is difficult to estimate parameters due to slow convergence.
Tracking and tracing in the UK: a dynamic causal modelling study
Date: 2020-05-16 00:00:00
Tasks: Time Series Analysis

By equipping a previously reported dynamic causal model of COVID-19 with an
isolation state, we modelled the effects of self-isolation consequent on
tracking and tracing. Specifically, we included a quarantine or isolation state
occupied by people who believe they might be infected but are asymptomatic, and
only leave if they test negative. We recovered maximum posteriori estimates of
the model parameters using time series of new cases, daily deaths, and tests
for the UK. These parameters were used to simulate the trajectory of the
outbreak in the UK over an 18-month period. Several clear-cut conclusions
emerged from these simulations. For example, under plausible (graded)
relaxations of social distancing, a rebound of infections within weeks is
unlikely. The emergence of a later second wave depends almost exclusively on
the rate at which we lose immunity, inherited from the first wave. There exists
no testing strategy that can attenuate mortality rates, other than by deferring
or delaying a second wave. A sufficiently powerful tracking and tracing
policy--implemented at the time of writing (10th May 2020)--will defer any
second wave beyond a time horizon of 18 months. Crucially, this deferment is
within current testing capabilities (requiring an efficacy of tracing and
tracking of about 20% of asymptomatic infected cases, with less than 50,000
tests per day). These conclusions are based upon a dynamic causal model for
which we provide some construct and face validation, using a comparative
analysis of the United Kingdom and Germany, supplemented with recent
serological studies.
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs
Authors: I. Dey, H. Joshi, Member, N. Marchetti, Senior Member, IEEE
Date: 2020-05-16 00:00:00
Tasks: Diversity, STS

In this letter, we propose space-time spreading (STS) of local sensor decisions before reporting them over a wireless multiple access channel (MAC), in order to achieve flexible balance between diversity and multiplexing gain as well as eliminate any chance of intrinsic interference inherent in MAC scenarios. Spreading of the sensor decisions using dispersion vectors exploits the benefits of multi-slot decision to improve low-complexity diversity gain and opportunistic throughput. On the other hand, at the receive side of the reporting channel, we formulate and compare optimum and sub-optimum fusion rules for arriving at a reliable conclusion.Simulation results demonstrate gain in performance with STS aided transmission from a minimum of 3 times to a maximum of 6 times over performance without STS.
Heart Rate Variability as a Predictive Biomarker of Thermal Comfort
Authors: Kizito Nkurikiyeyezu, Yuta Suzuki, Guillaume Lopez
Date: 2020-05-16 00:00:00
Tasks: Heart Rate Variability

Thermal comfort is an assessment of one's satisfaction with the surroundings; yet, most mechanisms that are used to provide thermal comfort are based on approaches that preclude physiological, psychological, and personal psychophysics that are precursors to thermal comfort. This leads to many people feeling either cold or hot in an environment that was supposed to be thermally comfortable to most users. To address this problem, this paper proposes to use heart rate variability (HRV) as an alternative indicator of thermal comfort status. Since HRV is linked to homeostasis, we conjectured that people's thermal comfort could be more accurately estimated based on their heart rate variability (HRV). To test our hypothesis, we analyzed statistical, spectral, and nonlinear HRV indices of 17 human subjects doing light office work in a cold, neutral, and hot environment. The resulting HRV indices were used as inputs to machine learning classification algorithms. We observed that HRV is distinctively different depending on the thermal environment and that it is possible to reliably predict each subject's thermal state (cold, neutral, and hot) with up to 93.7% accuracy. The result of this study suggests that it could be possible to design automatic real-time thermal comfort controllers based on people's HRV.
Flocking of V-shaped and Echelon Northern Bald Ibises with Different Wingspans: Repositioning and Energy Saving
Date: 2020-05-16 00:00:00

V-shaped and echelon formations help migratory birds to consume less energy
for migration. As the case study, the formation flight of the Northern Bald
Ibises is considered to investigate different effects on their flight
efficiency. The effects of the wingtip spacing and wingspan are examined on the
individual drag of each Ibis in the flock. Two scenarios are considered in this
study, (1) increasing and (2) decreasing wingspans toward the tail. An
algorithm is applied for replacement mechanism and load balancing of the Ibises
during their flight. In this replacing mechanism, the Ibises with the highest
value of remained energy are replaced with the Ibises with the lowest energy,
iteratively. The results indicate that depending on the positions of the birds
with various sizes in the flock, they consume a different level of energy.
Moreover, it is found that also small birds have the chance to take the lead
during the flock.
Artificial neural networks for 3D cell shape recognition from confocal images
Date: 2020-05-28 00:00:00
Tasks: Diagnostic

We present a dual-stage neural network architecture for analyzing fine shape
details from microscopy recordings in 3D. The system, tested on red blood
cells, uses training data from both healthy donors and patients with a
congenital blood disease. Characteristic shape features are revealed from the
spherical harmonics spectrum of each cell and are automatically processed to
create a reproducible and unbiased shape recognition and classification for
diagnostic and theragnostic use.
Information content of high-order associations of the human gut microbiota network
Authors: Weston D. Viles, Juliette C. Madan, Hongzhe Li, Jason C. Moore, Margaret R. Karagas, Anne G. Hoen
Date: 2020-05-16 00:00:00

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intra-species molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.
Attack-Resilient State Estimation with Intermittent Data Authentication
Date: 2020-05-16 00:00:00
Tasks: State Estimation

Network-based attacks on control systems may alter sensor data delivered to
the controller, effectively causing degradation in control performance. As a
result, having access to accurate state estimates, even in the presence of
attacks on sensor measurements, is of critical importance. In this paper, we
analyze performance of resilient state estimators (RSEs) when any subset of
sensors may be compromised by a stealthy attacker. Specifically, we consider
systems with the well-known l0-based RSE and two commonly used sound intrusion
detectors (IDs). For linear time-invariant plants with bounded noise, we define
the notion of perfect attackability (PA) when attacks may result in unbounded
estimation errors while remaining undetected by the employed ID (i.e.,
stealthy). We derive necessary and sufficient PA conditions, showing that a
system can be perfectly attackable even if the plant is stable. While PA can be
prevented with the use the standard cryptographic mechanisms (e.g.,message
authentication) that ensure data integrity under network-based attacks, their
continuous use imposes significant communication and computational overhead.
Consequently, we also study the impact that even intermittent use of data
authentication has on RSE performance guarantees in the presence of stealthy
attacks. We show that if messages from some of the sensors are even
intermittently authenticated, stealthy attacks could not result in unbounded
state estimation errors.
Parameter Estimation from ICC curves
Authors: Joceline Lega
Date: 2020-05-17 00:00:00
Tasks: parameter estimation

Incidence vs Cumulative Cases (ICC) curves are introduced and shown to provide a simple framework for parameter identification in the case of the most elementary epidemiological model, consisting of susceptible, infected, and removed compartments. This novel methodology is used to estimate the basic reproduction ratio of recent outbreaks, including the ongoing COVID-19 epidemic.
Harmonic Mitigation Schemes for Wind Power Plants by Embedding Control in Wind Turbines
Date: 2020-06-15 00:00:00

Harmonic pollution may damage the electric devices in wind power plants
(WPPs), and propagate to the external grid. This paper proposes a harmonic
mitigation scheme by embedding harmonic control functions in wind turbines
(WTs) to manage the harmonics in WPPs. It can improve the power quality at the
remote Point of Common Coupling (PCC), regulated by grid codes. The proposed
scheme detects the harmonics at WT buses and PCC based on instantaneous
measurements, and calculates the required compensation currents. Both the
general compensation scheme for reducing total harmonic distortion at the local
WT buses and the specific compensation scheme for reducing the selected-order
harmonics at the remote PCC are combined in the proposed harmonic mitigation
scheme. Besides, a phase correction algorithm using the frequency-dependent
model is proposed to compensate the phase differences between local WT buses
and remote PCC. A model of offshore WPP using manufacture's field-measurement
data is implemented in DIgSILENT/PowerFactory to validate the effectiveness of
the proposed harmonic mitigation scheme.
Identification/Segmentation of Indian Regional Languages with Singular Value Decomposition based Feature Embedding
Date: 2020-05-17 00:00:00
Tasks: Language Identification, Segmentation

language identification (LID) is identifing a language in a given spoken
utterance. Language segmentation is equally inportant as language
identification where language boundaries can be spotted in a multi language
utterance. In this paper, we have experimented with two schemes for language
identification in Indian regional language context as very few works has been
done. Singular value based feature embedding is used for both of the schemes.
In first scheme, the singular value decomposition (SVD) is applied to the
n-gram utterance matrix and in the second scheme, SVD is applied on the
difference supervector matrix space. We have observed that in both the schemes,
55-65% singular value energy is sufficient to capture the language context. In
n-gram based feature representation, we have seen that different skipgram
models capture different language context. We have observed that for short test
duration, supervector based feature representation is better but with a longer
duration test signal, n-gram based feature performed better. We have also
extended our work to explore language-based segmentation where we have seen
that segmentation accuracy of four language group with ten language training
model, scheme-1 has performed well but with same four language training model,
scheme-2 outperformed scheme-1
Multi-Gigabit Wireline Systems over Copper: An Interference Cancellation Perspective
Authors: S. M. Zafaruddin, Amir Leshem
Date: 2020-05-17 00:00:00

Interference cancellation is the main driving technology in enhancing the transmission rates over telephone lines above 100 Mbps. Still, crosstalk interference in multi-pair digital subscriber line (DSL) systems at higher frequencies has not been dealt with sufficiently. The upcoming G.(mg) fast DSL system envisions the use of extremely high bandwidth and full-duplex transmissions generating significantly higher crosstalk and self-interference signals. More powerful interference cancellation techniques are required to enable multi-gigabit per second data rate transmission over copper lines. In this article, we analyze the performance of interference cancellation techniques, with a focus on novel research approaches and design considerations for efficient interference mitigation for multi-gigabit transmission over standard copper lines. We also detail novel approaches for interference cancellation in the upcoming technologies.