pwc-archive/papers-with-abstracts huggingface dataset:
Asymptotic Security of Control Systems by Covert Reaction: Repeated Signaling Game with Undisclosed Belief
Date: 2020-03-25 00:00:00

This study investigates the relationship between resilience of control
systems to attacks and the information available to malicious attackers.
Specifically, it is shown that control systems are guaranteed to be secure in
an asymptotic manner by rendering reactions against potentially harmful actions
covert. The behaviors of the attacker and the defender are analyzed through a
repeated signaling game with an undisclosed belief under covert reactions. In
the typical setting of signaling games, reactions conducted by the defender are
supposed to be public information and the measurability enables the attacker to
accurately trace transitions of the defender's belief on existence of a
malicious attacker. In contrast, the belief in the game considered in this
paper is undisclosed and hence common equilibrium concepts can no longer be
employed for the analysis. To surmount this difficulty, a novel framework for
decision of reasonable strategies of the players in the game is introduced.
Based on the presented framework, it is revealed that any reasonable strategy
chosen by a rational malicious attacker converges to the benign behavior as
long as the reactions performed by the defender are unobservable to the
attacker. The result provides an explicit relationship between resilience and
information, which indicates the importance of covertness of reactions for
designing secure control systems.
Early warning signals for desynchronization in periodically forced systems
Date: 2020-03-25 00:00:00

Conditions such as insomnia, cardiac arrhythmia and jet-lag share a common
feature: they are all related to the ability of biological systems to
synchronize with the day-night cycle. When organisms lose resilience, this
ability of synchronizing can become weaker till they eventually become
desynchronized in a state of malfunctioning or sickness. It would be useful to
measure this loss of resilience before the full desynchronization takes place.
Several dynamical indicators of resilience (DIORs) have been proposed to
account for the loss of resilience of a dynamical system. The performance of
these indicators depends on the underlying mechanism of the critical
transition, usually a saddle-node bifurcation. Before such bifurcation the
recovery rate from perturbations of the system becomes slower, a mechanism
known as critical slowing down. Here we show that, for a wide class of
biological systems, desynchronization happens through another bifurcation,
namely the saddle-node of cycles, for which critical slowing down cannot be
directly detected. Such a bifurcation represents a system transitioning from
synchronized (phase locked) to a desynchronized state, or vice versa. We show
that after an appropriate transformation we can also detect this bifurcation
using dynamical indicators of resilience. We test this method with data
generated by models of sleep-wake cycles.
Cooperative Hypothesis Testing by Two Observers with Asymmetric Information
Authors: Aneesh Raghavan, John S. Baras
Date: 2020-03-25 00:00:00
Tasks: Vocal Bursts Valence Prediction

We consider the binary hypothesis testing problem with two observers. There are two possible states of nature (or hypotheses). Observations collected by the two observers are statistically related to the true state of nature. The knowledge of joint distribution of the observations collected and the true state of nature is unknown to the observers. There are two problems to be solved by the observers: (i) true state of nature is known: find the distribution of the local information collected; (ii) true state of nature is unknown: collaboratively estimate the same using the distributions found by solving the first problem. We present four algorithms, each having two phases where the two problems are solved, with emphasis on the information exchange between the observers and resulting patterns. We prove different properties of the algorithms including the following: the probability spaces constructed as a consequence of solving the first problem are dependent on the information patterns at the observers; (ii) the rate of decay of probability of error of algorithms while solving the second problem is dependent on the information exchange between the observers. We present a numerical example demonstrating the four algorithms.
The modelling of COVID19 pathways sheds light on mechanisms, opportunities and on controversial interpretations of medical treatments. v2
Authors: Maria Luisa Chiusano
Date: 2020-03-25 00:00:00

The new coronavirus (2019-nCoV or SARS-CoV2), inducing the current pandemic disease (COVID-19) and causing pneumoniae in humans, is dramatically increasing in epidemic scale since its first appearance in Wuhan, China, in December 2019. The first infection from epidemic coronaviruses in 2003 fostered the spread of an overwhelming amount of related scientific efforts. The manifold aspects that have been raised, as well as their redundancy offer precious information that has been underexploited and needs to be critically re-evaluated, appropriately used and offered to the whole community, from scientists, to medical doctors, stakeholders and common people. These efforts will favour a holistic view on the comprehension, prevention and development of strategies (pharmacological, clinical etc) as well as common intervention against the new coronavirus spreading. Here we describe a model that emerged from our analysis that was focused on the Renin Angiotensin System (RAS) and the possible routes linking it to the viral infection. because the infection is mediated by the viral receptor on human cell membranes Angiotensin Converting Enzyme (ACE2), which is a key component in RAS signalling. The model depicts the main pathways determining the disease and the molecular framework for its establishment, and can help to shed light on mechanisms involved in the infection. It promptly gives an answer to some of the controversial, and still open, issues concerning predisposing conditions and medical treatments that protect from or favour the severity of the disease (such as the use of ACE inhibitors or ARBs/sartans), or to the sex related biases in the affected population. The model highlights novel opportunities for further investigations, diagnosis and appropriate intervention to understand and fight COVID19.
Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction
Date: 2020-03-25 00:00:00

In this paper, we consider the data-driven model invalidation problem for
Lipschitz continuous systems, where instead of given mathematical models, only
prior noisy sampled data of the systems are available. We show that this
data-driven model invalidation problem can be solved using a tractable
feasibility check. Our proposed approach consists of two main components: (i) a
data-driven abstraction part that uses the noisy sampled data to
over-approximate the unknown Lipschitz continuous dynamics with upper and lower
functions, and (ii) an optimization-based model invalidation component that
determines the incompatibility of the data-driven abstraction with a newly
observed length-T output trajectory. Finally, we discuss several methods to
reduce the computational complexity of the algorithm and demonstrate their
effectiveness with a simulation example of swarm intent identification.
Energy Efficiency Maximization in Millimeter Wave Hybrid MIMO Systems for 5G and Beyond
Date: 2020-03-26 00:00:00

At millimeter wave (mmWave) frequencies, the higher cost and power
consumption of hardware components in multiple-input multiple output (MIMO)
systems do not allow beamforming entirely at the baseband with a separate radio
frequency (RF) chain for each antenna. In such scenarios, to enable spatial
multiplexing, hybrid beamforming, which uses phase shifters to connect a fewer
number of RF chains to a large number of antennas is a cost effective and
energy-saving alternative. This paper describes our research on fully adaptive
transceivers that adapt their behaviour on a frame-by-frame basis, so that a
mmWave hybrid MIMO system always operates in the most energy efficient manner.
Exhaustive search based brute force approach is computationally intensive, so
we study fractional programming as a low-cost alternative to solve the problem
which maximizes energy efficiency. The performance results indicate that the
resulting mmWave hybrid MIMO transceiver achieves significantly improved energy
efficiency results compared to the baseline cases involving analogue-only or
digital-only signal processing solutions, and shows performance trade-offs with
the brute force approach.
Recursive Star-Identification Algorithm using an Adaptive SVD-based Angular Velocity Estimator
Authors: Hunter Johnston, Carl Leake, Marcelino M. de Almeida, Daniele Mortari
Date: 2020-03-26 00:00:00

This paper describes an algorithm obtained by merging a recursive star identification algorithm with a recently developed adaptive SVD-based estimator of the angular velocity vector (QuateRA). In a recursive algorithm, the more accurate the angular velocity estimate, the quicker and more robust to noise the resultant recursive algorithm is. Hence, combining these two techniques produces an algorithm capable of handling a variety of dynamics scenarios. The speed and robustness of the algorithm are highlighted in a selection of simulated scenarios. First, a speed comparison is made with the state-of-the-art lost-in-space star identification algorithm, Pyramid. This test shows that in the best case the algorithm is on average an order of magnitude faster than Pyramid. Next, the recursive algorithm is validated for a variety of dynamic cases including a ground-based "Stellar Compass" scenario, a satellite in geosynchronous orbit, a satellite during a re-orientation maneuver, and a satellite undergoing non-pure-spin dynamics.
Comments on A New Parity Check Stopping Criterion for Turbo Decoding
Date: 2020-03-26 00:00:00

A parity-check stopping (PCS) criterion for turbo decoding is proposed in
[1], which shows its priority compared with the stopping criteria of Sign
Change Ratio (SCR), Sign Difference Ratio (SDR), Cross Entropy (CE) and
improved CEbased (Yu) method. But another well-known simple stopping criterion
named Hard-Decision-Aided (HDA) criterion has not been compared in [1]. In this
letter, through analysis we show that using max-log-MAP algorithm, PCS is
equivalent to HDA; while simulations demonstrate that using log-MAP algorithm,
PCS has nearly the same performance as HDA.
A Sequential Subspace Method for Millimeter Wave MIMO Channel Estimation
Date: 2020-03-26 00:00:00

Data transmission over the mmWave in fifth-generation wireless networks aims
to support very high speed wireless communications. A substantial increase in
spectrum efficiency for mmWave transmission can be achieved by using advanced
hybrid precoding, for which accurate channel state information is the key.
Rather than estimating the entire channel matrix, directly estimating subspace
information, which contains fewer parameters, does have enough information to
design transceivers. However, the large channel use overhead and associated
computational complexity in the existing channel subspace estimation techniques
are major obstacles to deploy the subspace approach for channel estimation. In
this paper, we propose a sequential two-stage subspace estimation method that
can resolve the overhead issues and provide accurate subspace information.
Utilizing a sequential method enables us to avoid manipulating the entire
high-dimensional training signal, which greatly reduces the complexity.
Specifically, in the first stage, the proposed method samples the columns of
channel matrix to estimate its column subspace. Then, based on the obtained
column subspace, it optimizes the training signals to estimate the row
subspace. For a channel with $N_r$ receive antennas and $N_t$ transmit
antennas, our analysis shows that the proposed technique only requires $O(N_t)$
channel uses, while providing a guarantee of subspace estimation accuracy. By
theoretical analysis, it is shown that the similarity between the estimated
subspace and the true subspace is linearly related to the signal-to-noise ratio
(SNR), i.e., $O(\text{SNR})$, at high SNR, while quadratically related to the
SNR, i.e., $O(\text{SNR}^2)$, at low SNR. Simulation results show that the
proposed sequential subspace method can provide improved subspace accuracy,
normalized mean squared error, and spectrum efficiency over existing methods.
Sub-pixel detection in hyperspectral imaging with elliptically contoured $t$-distributed background
Date: 2020-03-26 00:00:00

Detection of a target with known spectral signature when this target may
occupy only a fraction of the pixel is an important issue in hyperspectral
imaging. We recently derived the generalized likelihood ratio test (GLRT) for
such sub-pixel targets, either for the so-called replacement model where the
presence of a target induces a decrease of the background power, due to the sum
of abundances equal to one, or for a mixed model which alleviates some of the
limitations of the replacement model. In both cases, the background was assumed
to be Gaussian distributed. The aim of this short communication is to extend
these detectors to the broader class of elliptically contoured distributions,
more precisely matrix-variate $t$-distributions with unknown mean and
covariance matrix. We show that the generalized likelihood ratio tests in the
$t$-distributed case coincide with their Gaussian counterparts, which confers
the latter an increased generality for application. The performance as well as
the robustness of these detectors are evaluated through numerical simulations.
Mitigating Fiber Nonlinearities by Short-length Probabilistic Shaping
Date: 2020-03-26 00:00:00

We show that short-length probabilistic shaping reduces nonlinear
interference in optical fiber transmission. SNR improvements of up to 0.8 dB
are obtained. The shaping gain vanishes when interleaving is employed and not
undone before transmission.
Iterative learning control in prosumer-based microgrids with hierarchical control
Date: 2020-06-26 00:00:00

Power systems are subject to fundamental changes due to the increasing infeed
of renewable energy sources. Taking the accompanying decentralization of power
generation into account, the concept of prosumer-based microgrids gives the
opportunity to rethink structuring and operation of power systems from scratch.
In a prosumer-based microgrid, each power grid node can feed energy into the
grid and draw energy from the grid. The concept allows for spatial aggregation
such that also an interaction between microgrids can be represented as a
prosumer-based microgrid. The contribution of this work is threefold: (i) we
propose a decentralized hierarchical control approach in a network including
different time scales, (ii) we use iterative learning control to compensate
periodic demand patterns and save lower layer control energy and (iii) we
assure asymptotic stability and monotonic convergence in the iteration domain
for the linearized dynamics and validate the performance by simulating the
nonlinear dynamics.
Estimation of genome size using k-mer frequencies from corrected long reads
Authors: Hengchao Wang, Bo Liu, Yan Zhang, Fan Jiang, Yuwei Ren, Lijuan Yin, Hangwei Liu, Sen Wang, Wei Fan
Date: 2020-03-26 00:00:00

The third-generation long reads sequencing technologies, such as PacBio and Nanopore, have great advantages over second-generation Illumina sequencing in de novo assembly studies. However, due to the inherent low base accuracy, third-generation sequencing data cannot be used for k-mer counting and estimating genomic profile based on k-mer frequencies. Thus, in current genome projects, second-generation data is also necessary for accurately determining genome size and other genomic characteristics. We show that corrected third-generation data can be used to count k-mer frequencies and estimate genome size reliably, in replacement of using second-generation data. Therefore, future genome projects can depend on only one sequencing technology to finish both assembly and k-mer analysis, which will largely decrease sequencing cost in both time and money. Moreover, we present a fast light-weight tool kmerfreq and use it to perform all the k-mer counting tasks in this work. We have demonstrated that corrected third-generation sequencing data can be used to estimate genome size and developed a new open-source C/C++ k-mer counting tool, kmerfreq, which is freely available at https://github.com/fanagislab/kmerfreq.
Payload-agnostic Decoupling and Hybrid Vibration Isolation Control for a Maglev Platform with Redundant Actuation
Date: 2020-03-31 00:00:00

Payload-specific vibration control may be suitable for a particular task but
lacks generality and transferability required for adapting to the various
payload. Self-decoupling and robust vibration control are the crucial problems
to achieve payload-agnostic vibration control. However, there are problems
still unsolved.
  In this article, we present a maglev vibration isolation platform (MVIP),
which aims to attenuate vibration in the payload-agnostic task under a dynamic
environment. Since efforts trying to suppress disturbance will encounter
inevitable coupling problems, we analyzed the reasons resulting in it and
proposed unique and effective solutions.
  To achieve payload-agnostic vibration control, we proposed a new control
strategy, which is the main contribution of this article. It consists of a
self-construct radial basis function neural network inversion (SRBFNNI)
decoupling scheme and hybrid adaptive feed-forward internal model control
(HAFIMC). The former one enables the MVIP to create a self inverse model with
little prior knowledge and achieving self-decoupling. For the unique structure
of MVIP, the vibration control problem is stated and addressed by the proposed
HAFIMC, which utilizes the adaptive part to deal with the periodical
disturbance and the internal mode part to deal with the stability.
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience
Authors: Bruno Golosio, Chiara De Luca, Cristiano Capone, Elena Pastorelli, Giovanni Stegel, Gianmarco Tiddia, Giulia De Bonis, Pier Stanislao Paolucci
Date: 2020-03-26 00:00:00
Tasks: Incremental Learning

The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories.
Information and Communication Theoretical Understanding and Treatment of Spinal Cord Injuries: State-of-the-art and Research Challenges
Date: 2020-03-26 00:00:00

Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio- Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research.
Bounded State Estimation over Finite-State Channels: Relating Topological Entropy and Zero-Error Capacity
Authors: Amir Saberi, Farhad Farokhi, Girish N. Nair
Date: 2020-03-24 00:00:00
Tasks: State Estimation

We investigate state estimation of linear systems over channels having a finite state not known by the transmitter or receiver. We show that similar to memoryless channels, zero-error capacity is the right figure of merit for achieving bounded estimation errors. We then consider finite-state, worst-case versions of the common erasure and additive noise channels models, in which the noise is governed by a finite-state machine without any statistical structure. Upper and lower bounds on their zero-error capacities are derived, revealing a connection with the {\em topological entropy} of the channel dynamics. Separate necessary and sufficient conditions for bounded linear state estimation errors via such channels are obtained. These estimation conditions bring together the topological entropies of the linear system and the discrete channel.
Hybrid Precoding For Millimeter Wave MIMO Systems: A Matrix Factorization Approach
Date: 2020-03-26 00:00:00

This paper investigates the hybrid precoding design for millimeter wave
(mmWave) multiple-input multiple-output (MIMO) systems with finite-alphabet
inputs. The precoding problem is a joint optimization of analog and digital
precoders, and we treat it as a matrix factorization problem with power and
constant modulus constraints. Our work presents three main contributions:
First, we present a sufficient condition and a necessary condition for hybrid
precoding schemes to realize unconstrained optimal precoders exactly when the
number of data streams Ns satisfies Ns = minfrank(H);Nrfg, where H represents
the channel matrix and Nrf is the number of radio frequency (RF) chains.
Second, we show that the coupled power constraint in our matrix factorization
problem can be removed without loss of optimality. Third, we propose a
Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based algorithm to solve our matrix
factorization problem using gradient and Hessian information. Several numerical
results are provided to show that our proposed algorithm outperforms existing
hybrid precoding algorithms.